• Calling on LLMs: New NVIDIA AI Blueprint Helps Automate Telco Network Configuration

    Telecom companies last year spent nearly billion in capital expenditures and over trillion in operating expenditures.
    These large expenses are due in part to laborious manual processes that telcos face when operating networks that require continuous optimizations.
    For example, telcos must constantly tune network parameters for tasks — such as transferring calls from one network to another or distributing network traffic across multiple servers — based on the time of day, user behavior, mobility and traffic type.
    These factors directly affect network performance, user experience and energy consumption.
    To automate these optimization processes and save costs for telcos across the globe, NVIDIA today unveiled at GTC Paris its first AI Blueprint for telco network configuration.
    At the blueprint’s core are customized large language models trained specifically on telco network data — as well as the full technical and operational architecture for turning the LLMs into an autonomous, goal-driven AI agent for telcos.
    Automate Network Configuration With the AI Blueprint
    NVIDIA AI Blueprints — available on build.nvidia.com — are customizable AI workflow examples. They include reference code, documentation and deployment tools that show enterprise developers how to deliver business value with NVIDIA NIM microservices.
    The AI Blueprint for telco network configuration — built with BubbleRAN 5G solutions and datasets — enables developers, network engineers and telecom providers to automatically optimize the configuration of network parameters using agentic AI.
    This can streamline operations, reduce costs and significantly improve service quality by embedding continuous learning and adaptability directly into network infrastructures.
    Traditionally, network configurations required manual intervention or followed rigid rules to adapt to dynamic network conditions. These approaches limited adaptability and increased operational complexities, costs and inefficiencies.
    The new blueprint helps shift telco operations from relying on static, rules-based systems to operations based on dynamic, AI-driven automation. It enables developers to build advanced, telco-specific AI agents that make real-time, intelligent decisions and autonomously balance trade-offs — such as network speed versus interference, or energy savings versus utilization — without human input.
    Powered and Deployed by Industry Leaders
    Trained on 5G data generated by BubbleRAN, and deployed on the BubbleRAN 5G O-RAN platform, the blueprint provides telcos with insight on how to set various parameters to reach performance goals, like achieving a certain bitrate while choosing an acceptable signal-to-noise ratio — a measure that impacts voice quality and thus user experience.
    With the new AI Blueprint, network engineers can confidently set initial parameter values and update them as demanded by continuous network changes.
    Norway-based Telenor Group, which serves over 200 million customers globally, is the first telco to integrate the AI Blueprint for telco network configuration as part of its initiative to deploy intelligent, autonomous networks that meet the performance and agility demands of 5G and beyond.
    “The blueprint is helping us address configuration challenges and enhance quality of service during network installation,” said Knut Fjellheim, chief technology innovation officer at Telenor Maritime. “Implementing it is part of our push toward network automation and follows the successful deployment of agentic AI for real-time network slicing in a private 5G maritime use case.”
    Industry Partners Deploy Other NVIDIA-Powered Autonomous Network Technologies
    The AI Blueprint for telco network configuration is just one of many announcements at NVIDIA GTC Paris showcasing how the telecom industry is using agentic AI to make autonomous networks a reality.
    Beyond the blueprint, leading telecom companies and solutions providers are tapping into NVIDIA accelerated computing, software and microservices to provide breakthrough innovations poised to vastly improve networks and communications services — accelerating the progress to autonomous networks and improving customer experiences.
    NTT DATA is powering its agentic platform for telcos with NVIDIA accelerated compute and the NVIDIA AI Enterprise software platform. Its first agentic use case is focused on network alarms management, where NVIDIA NIM microservices help automate and power observability, troubleshooting, anomaly detection and resolution with closed loop ticketing.
    Tata Consultancy Services is delivering agentic AI solutions for telcos built on NVIDIA DGX Cloud and using NVIDIA AI Enterprise to develop, fine-tune and integrate large telco models into AI agent workflows. These range from billing and revenue assurance, autonomous network management to hybrid edge-cloud distributed inference.
    For example, the company’s anomaly management agentic AI model includes real-time detection and resolution of network anomalies and service performance optimization. This increases business agility and improves operational efficiencies by up to 40% by eliminating human intensive toils, overheads and cross-departmental silos.
    Prodapt has introduced an autonomous operations workflow for networks, powered by NVIDIA AI Enterprise, that offers agentic AI capabilities to support autonomous telecom networks. AI agents can autonomously monitor networks, detect anomalies in real time, initiate diagnostics, analyze root causes of issues using historical data and correlation techniques, automatically execute corrective actions, and generate, enrich and assign incident tickets through integrated ticketing systems.
    Accenture announced its new portfolio of agentic AI solutions for telecommunications through its AI Refinery platform, built on NVIDIA AI Enterprise software and accelerated computing.
    The first available solution, the NOC Agentic App, boosts network operations center tasks by using a generative AI-driven, nonlinear agentic framework to automate processes such as incident and fault management, root cause analysis and configuration planning. Using the Llama 3.1 70B NVIDIA NIM microservice and the AI Refinery Distiller Framework, the NOC Agentic App orchestrates networks of intelligent agents for faster, more efficient decision-making.
    Infosys is announcing its agentic autonomous operations platform, called Infosys Smart Network Assurance, designed to accelerate telecom operators’ journeys toward fully autonomous network operations.
    ISNA helps address long-standing operational challenges for telcos — such as limited automation and high average time to repair — with an integrated, AI-driven platform that reduces operational costs by up to 40% and shortens fault resolution times by up to 30%. NVIDIA NIM and NeMo microservices enhance the platform’s reasoning and hallucination-detection capabilities, reduce latency and increase accuracy.
    Get started with the new blueprint today.
    Learn more about the latest AI advancements for telecom and other industries at NVIDIA GTC Paris, running through Thursday, June 12, at VivaTech, including a keynote from NVIDIA founder and CEO Jensen Huang and a special address from Ronnie Vasishta, senior vice president of telecom at NVIDIA. Plus, hear from industry leaders in a panel session with Orange, Swisscom, Telenor and NVIDIA.
    #calling #llms #new #nvidia #blueprint
    Calling on LLMs: New NVIDIA AI Blueprint Helps Automate Telco Network Configuration
    Telecom companies last year spent nearly billion in capital expenditures and over trillion in operating expenditures. These large expenses are due in part to laborious manual processes that telcos face when operating networks that require continuous optimizations. For example, telcos must constantly tune network parameters for tasks — such as transferring calls from one network to another or distributing network traffic across multiple servers — based on the time of day, user behavior, mobility and traffic type. These factors directly affect network performance, user experience and energy consumption. To automate these optimization processes and save costs for telcos across the globe, NVIDIA today unveiled at GTC Paris its first AI Blueprint for telco network configuration. At the blueprint’s core are customized large language models trained specifically on telco network data — as well as the full technical and operational architecture for turning the LLMs into an autonomous, goal-driven AI agent for telcos. Automate Network Configuration With the AI Blueprint NVIDIA AI Blueprints — available on build.nvidia.com — are customizable AI workflow examples. They include reference code, documentation and deployment tools that show enterprise developers how to deliver business value with NVIDIA NIM microservices. The AI Blueprint for telco network configuration — built with BubbleRAN 5G solutions and datasets — enables developers, network engineers and telecom providers to automatically optimize the configuration of network parameters using agentic AI. This can streamline operations, reduce costs and significantly improve service quality by embedding continuous learning and adaptability directly into network infrastructures. Traditionally, network configurations required manual intervention or followed rigid rules to adapt to dynamic network conditions. These approaches limited adaptability and increased operational complexities, costs and inefficiencies. The new blueprint helps shift telco operations from relying on static, rules-based systems to operations based on dynamic, AI-driven automation. It enables developers to build advanced, telco-specific AI agents that make real-time, intelligent decisions and autonomously balance trade-offs — such as network speed versus interference, or energy savings versus utilization — without human input. Powered and Deployed by Industry Leaders Trained on 5G data generated by BubbleRAN, and deployed on the BubbleRAN 5G O-RAN platform, the blueprint provides telcos with insight on how to set various parameters to reach performance goals, like achieving a certain bitrate while choosing an acceptable signal-to-noise ratio — a measure that impacts voice quality and thus user experience. With the new AI Blueprint, network engineers can confidently set initial parameter values and update them as demanded by continuous network changes. Norway-based Telenor Group, which serves over 200 million customers globally, is the first telco to integrate the AI Blueprint for telco network configuration as part of its initiative to deploy intelligent, autonomous networks that meet the performance and agility demands of 5G and beyond. “The blueprint is helping us address configuration challenges and enhance quality of service during network installation,” said Knut Fjellheim, chief technology innovation officer at Telenor Maritime. “Implementing it is part of our push toward network automation and follows the successful deployment of agentic AI for real-time network slicing in a private 5G maritime use case.” Industry Partners Deploy Other NVIDIA-Powered Autonomous Network Technologies The AI Blueprint for telco network configuration is just one of many announcements at NVIDIA GTC Paris showcasing how the telecom industry is using agentic AI to make autonomous networks a reality. Beyond the blueprint, leading telecom companies and solutions providers are tapping into NVIDIA accelerated computing, software and microservices to provide breakthrough innovations poised to vastly improve networks and communications services — accelerating the progress to autonomous networks and improving customer experiences. NTT DATA is powering its agentic platform for telcos with NVIDIA accelerated compute and the NVIDIA AI Enterprise software platform. Its first agentic use case is focused on network alarms management, where NVIDIA NIM microservices help automate and power observability, troubleshooting, anomaly detection and resolution with closed loop ticketing. Tata Consultancy Services is delivering agentic AI solutions for telcos built on NVIDIA DGX Cloud and using NVIDIA AI Enterprise to develop, fine-tune and integrate large telco models into AI agent workflows. These range from billing and revenue assurance, autonomous network management to hybrid edge-cloud distributed inference. For example, the company’s anomaly management agentic AI model includes real-time detection and resolution of network anomalies and service performance optimization. This increases business agility and improves operational efficiencies by up to 40% by eliminating human intensive toils, overheads and cross-departmental silos. Prodapt has introduced an autonomous operations workflow for networks, powered by NVIDIA AI Enterprise, that offers agentic AI capabilities to support autonomous telecom networks. AI agents can autonomously monitor networks, detect anomalies in real time, initiate diagnostics, analyze root causes of issues using historical data and correlation techniques, automatically execute corrective actions, and generate, enrich and assign incident tickets through integrated ticketing systems. Accenture announced its new portfolio of agentic AI solutions for telecommunications through its AI Refinery platform, built on NVIDIA AI Enterprise software and accelerated computing. The first available solution, the NOC Agentic App, boosts network operations center tasks by using a generative AI-driven, nonlinear agentic framework to automate processes such as incident and fault management, root cause analysis and configuration planning. Using the Llama 3.1 70B NVIDIA NIM microservice and the AI Refinery Distiller Framework, the NOC Agentic App orchestrates networks of intelligent agents for faster, more efficient decision-making. Infosys is announcing its agentic autonomous operations platform, called Infosys Smart Network Assurance, designed to accelerate telecom operators’ journeys toward fully autonomous network operations. ISNA helps address long-standing operational challenges for telcos — such as limited automation and high average time to repair — with an integrated, AI-driven platform that reduces operational costs by up to 40% and shortens fault resolution times by up to 30%. NVIDIA NIM and NeMo microservices enhance the platform’s reasoning and hallucination-detection capabilities, reduce latency and increase accuracy. Get started with the new blueprint today. Learn more about the latest AI advancements for telecom and other industries at NVIDIA GTC Paris, running through Thursday, June 12, at VivaTech, including a keynote from NVIDIA founder and CEO Jensen Huang and a special address from Ronnie Vasishta, senior vice president of telecom at NVIDIA. Plus, hear from industry leaders in a panel session with Orange, Swisscom, Telenor and NVIDIA. #calling #llms #new #nvidia #blueprint
    BLOGS.NVIDIA.COM
    Calling on LLMs: New NVIDIA AI Blueprint Helps Automate Telco Network Configuration
    Telecom companies last year spent nearly $295 billion in capital expenditures and over $1 trillion in operating expenditures. These large expenses are due in part to laborious manual processes that telcos face when operating networks that require continuous optimizations. For example, telcos must constantly tune network parameters for tasks — such as transferring calls from one network to another or distributing network traffic across multiple servers — based on the time of day, user behavior, mobility and traffic type. These factors directly affect network performance, user experience and energy consumption. To automate these optimization processes and save costs for telcos across the globe, NVIDIA today unveiled at GTC Paris its first AI Blueprint for telco network configuration. At the blueprint’s core are customized large language models trained specifically on telco network data — as well as the full technical and operational architecture for turning the LLMs into an autonomous, goal-driven AI agent for telcos. Automate Network Configuration With the AI Blueprint NVIDIA AI Blueprints — available on build.nvidia.com — are customizable AI workflow examples. They include reference code, documentation and deployment tools that show enterprise developers how to deliver business value with NVIDIA NIM microservices. The AI Blueprint for telco network configuration — built with BubbleRAN 5G solutions and datasets — enables developers, network engineers and telecom providers to automatically optimize the configuration of network parameters using agentic AI. This can streamline operations, reduce costs and significantly improve service quality by embedding continuous learning and adaptability directly into network infrastructures. Traditionally, network configurations required manual intervention or followed rigid rules to adapt to dynamic network conditions. These approaches limited adaptability and increased operational complexities, costs and inefficiencies. The new blueprint helps shift telco operations from relying on static, rules-based systems to operations based on dynamic, AI-driven automation. It enables developers to build advanced, telco-specific AI agents that make real-time, intelligent decisions and autonomously balance trade-offs — such as network speed versus interference, or energy savings versus utilization — without human input. Powered and Deployed by Industry Leaders Trained on 5G data generated by BubbleRAN, and deployed on the BubbleRAN 5G O-RAN platform, the blueprint provides telcos with insight on how to set various parameters to reach performance goals, like achieving a certain bitrate while choosing an acceptable signal-to-noise ratio — a measure that impacts voice quality and thus user experience. With the new AI Blueprint, network engineers can confidently set initial parameter values and update them as demanded by continuous network changes. Norway-based Telenor Group, which serves over 200 million customers globally, is the first telco to integrate the AI Blueprint for telco network configuration as part of its initiative to deploy intelligent, autonomous networks that meet the performance and agility demands of 5G and beyond. “The blueprint is helping us address configuration challenges and enhance quality of service during network installation,” said Knut Fjellheim, chief technology innovation officer at Telenor Maritime. “Implementing it is part of our push toward network automation and follows the successful deployment of agentic AI for real-time network slicing in a private 5G maritime use case.” Industry Partners Deploy Other NVIDIA-Powered Autonomous Network Technologies The AI Blueprint for telco network configuration is just one of many announcements at NVIDIA GTC Paris showcasing how the telecom industry is using agentic AI to make autonomous networks a reality. Beyond the blueprint, leading telecom companies and solutions providers are tapping into NVIDIA accelerated computing, software and microservices to provide breakthrough innovations poised to vastly improve networks and communications services — accelerating the progress to autonomous networks and improving customer experiences. NTT DATA is powering its agentic platform for telcos with NVIDIA accelerated compute and the NVIDIA AI Enterprise software platform. Its first agentic use case is focused on network alarms management, where NVIDIA NIM microservices help automate and power observability, troubleshooting, anomaly detection and resolution with closed loop ticketing. Tata Consultancy Services is delivering agentic AI solutions for telcos built on NVIDIA DGX Cloud and using NVIDIA AI Enterprise to develop, fine-tune and integrate large telco models into AI agent workflows. These range from billing and revenue assurance, autonomous network management to hybrid edge-cloud distributed inference. For example, the company’s anomaly management agentic AI model includes real-time detection and resolution of network anomalies and service performance optimization. This increases business agility and improves operational efficiencies by up to 40% by eliminating human intensive toils, overheads and cross-departmental silos. Prodapt has introduced an autonomous operations workflow for networks, powered by NVIDIA AI Enterprise, that offers agentic AI capabilities to support autonomous telecom networks. AI agents can autonomously monitor networks, detect anomalies in real time, initiate diagnostics, analyze root causes of issues using historical data and correlation techniques, automatically execute corrective actions, and generate, enrich and assign incident tickets through integrated ticketing systems. Accenture announced its new portfolio of agentic AI solutions for telecommunications through its AI Refinery platform, built on NVIDIA AI Enterprise software and accelerated computing. The first available solution, the NOC Agentic App, boosts network operations center tasks by using a generative AI-driven, nonlinear agentic framework to automate processes such as incident and fault management, root cause analysis and configuration planning. Using the Llama 3.1 70B NVIDIA NIM microservice and the AI Refinery Distiller Framework, the NOC Agentic App orchestrates networks of intelligent agents for faster, more efficient decision-making. Infosys is announcing its agentic autonomous operations platform, called Infosys Smart Network Assurance (ISNA), designed to accelerate telecom operators’ journeys toward fully autonomous network operations. ISNA helps address long-standing operational challenges for telcos — such as limited automation and high average time to repair — with an integrated, AI-driven platform that reduces operational costs by up to 40% and shortens fault resolution times by up to 30%. NVIDIA NIM and NeMo microservices enhance the platform’s reasoning and hallucination-detection capabilities, reduce latency and increase accuracy. Get started with the new blueprint today. Learn more about the latest AI advancements for telecom and other industries at NVIDIA GTC Paris, running through Thursday, June 12, at VivaTech, including a keynote from NVIDIA founder and CEO Jensen Huang and a special address from Ronnie Vasishta, senior vice president of telecom at NVIDIA. Plus, hear from industry leaders in a panel session with Orange, Swisscom, Telenor and NVIDIA.
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  • NVIDIA CEO Drops the Blueprint for Europe’s AI Boom

    At GTC Paris — held alongside VivaTech, Europe’s largest tech event — NVIDIA founder and CEO Jensen Huang delivered a clear message: Europe isn’t just adopting AI — it’s building it.
    “We now have a new industry, an AI industry, and it’s now part of the new infrastructure, called intelligence infrastructure, that will be used by every country, every society,” Huang said, addressing an audience gathered online and at the iconic Dôme de Paris.
    From exponential inference growth to quantum breakthroughs, and from infrastructure to industry, agentic AI to robotics, Huang outlined how the region is laying the groundwork for an AI-powered future.

    A New Industrial Revolution
    At the heart of this transformation, Huang explained, are systems like GB200 NVL72 — “one giant GPU” and NVIDIA’s most powerful AI platform yet — now in full production and powering everything from sovereign models to quantum computing.
    “This machine was designed to be a thinking machine, a thinking machine, in the sense that it reasons, it plans, it spends a lot of time talking to itself,” Huang said, walking the audience through the size and scale of these machines and their performance.
    At GTC Paris, Huang showed audience members the innards of some of NVIDIA’s latest hardware.
    There’s more coming, with Huang saying NVIDIA’s partners are now producing 1,000 GB200 systems a week, “and this is just the beginning.” He walked the audience through a range of available systems ranging from the tiny NVIDIA DGX Spark to rack-mounted RTX PRO Servers.
    Huang explained that NVIDIA is working to help countries use technologies like these to build both AI infrastructure — services built for third parties to use and innovate on — and AI factories, which companies build for their own use, to generate revenue.
    NVIDIA is partnering with European governments, telcos and cloud providers to deploy NVIDIA technologies across the region. NVIDIA is also expanding its network of technology centers across Europe — including new hubs in Finland, Germany, Spain, Italy and the U.K. — to accelerate skills development and quantum growth.
    Quantum Meets Classical
    Europe’s quantum ambitions just got a boost.
    The NVIDIA CUDA-Q platform is live on Denmark’s Gefion supercomputer, opening new possibilities for hybrid AI and quantum engineering. In addition, Huang announced that CUDA-Q is now available on NVIDIA Grace Blackwell systems.
    Across the continent, NVIDIA is partnering with supercomputing centers and quantum hardware builders to advance hybrid quantum-AI research and accelerate quantum error correction.
    “Quantum computing is reaching an inflection point,” Huang said. “We are within reach of being able to apply quantum computing, quantum classical computing, in areas that can solve some interesting problems in the coming years.”
    Sovereign Models, Smarter Agents
    European developers want more control over their models. Enter NVIDIA Nemotron, designed to help build large language models tuned to local needs.
    “And so now you know that you have access to an enhanced open model that is still open, that is top of the leader chart,” Huang said.
    These models will be coming to Perplexity, a reasoning search engine, enabling secure, multilingual AI deployment across Europe.
    “You can now ask and get questions answered in the language, in the culture, in the sensibility of your country,” Huang said.
    Huang explained how NVIDIA is helping countries across Europe build AI infrastructure.
    Every company will build its own agents, Huang said. To help create those agents, Huang introduced a suite of agentic AI blueprints, including an Agentic AI Safety blueprint for enterprises and governments.
    The new NVIDIA NeMo Agent toolkit and NVIDIA AI Blueprint for building data flywheels further accelerate the development of safe, high-performing AI agents.
    To help deploy these agents, NVIDIA is partnering with European governments, telcos and cloud providers to deploy the DGX Cloud Lepton platform across the region, providing instant access to accelerated computing capacity.
    “One model architecture, one deployment, and you can run it anywhere,” Huang said, adding that Lepton is now integrated with Hugging Face, giving developers direct access to global compute.
    The Industrial Cloud Goes Live
    AI isn’t just virtual. It’s powering physical systems, too, sparking a new industrial revolution.
    “We’re working on industrial AI with one company after another,” Huang said, describing work to build digital twins based on the NVIDIA Omniverse platform with companies across the continent.
    Huang explained that everything he showed during his keynote was “computer simulation, not animation” and that it looks beautiful because “it turns out the world is beautiful, and it turns out math is beautiful.”
    To further this work, Huang announced NVIDIA is launching the world’s first industrial AI cloud — to be built in Germany — to help Europe’s manufacturers simulate, automate and optimize at scale.
    “Soon, everything that moves will be robotic,” Huang said. “And the car is the next one.”
    NVIDIA DRIVE, NVIDIA’s full-stack AV platform, is now in production to accelerate the large-scale deployment of safe, intelligent transportation.
    And to show what’s coming next, Huang was joined on stage by Grek, a pint-sized robot, as Huang talked about how NVIDIA partnered with DeepMind and Disney to build Newton, the world’s most advanced physics training engine for robotics.
    The Next Wave
    The next wave of AI has begun — and it’s exponential, Huang explained.
    “We have physical robots, and we have information robots. We call them agents,” Huang said. “The technology necessary to teach a robot to manipulate, to simulate — and of course, the manifestation of an incredible robot — is now right in front of us.”
    This new era of AI is being driven by a surge in inference workloads. “The number of people using inference has gone from 8 million to 800 million — 100x in just a couple of years,” Huang said.
    To meet this demand, Huang emphasized the need for a new kind of computer: “We need a special computer designed for thinking, designed for reasoning. And that’s what Blackwell is — a thinking machine.”
    Huang and Grek, as he explained how AI is driving advancements in robotics.
    These Blackwell-powered systems will live in a new class of data centers — AI factories — built to generate tokens, the raw material of modern intelligence.
    “These AI factories are going to generate tokens,” Huang said, turning to Grek with a smile. “And these tokens are going to become your food, little Grek.”
    With that, the keynote closed on a bold vision: a future powered by sovereign infrastructure, agentic AI, robotics — and exponential inference — all built in partnership with Europe.
    Watch the NVIDIA GTC Paris keynote from Huang at VivaTech and explore GTC Paris sessions.
    #nvidia #ceo #drops #blueprint #europes
    NVIDIA CEO Drops the Blueprint for Europe’s AI Boom
    At GTC Paris — held alongside VivaTech, Europe’s largest tech event — NVIDIA founder and CEO Jensen Huang delivered a clear message: Europe isn’t just adopting AI — it’s building it. “We now have a new industry, an AI industry, and it’s now part of the new infrastructure, called intelligence infrastructure, that will be used by every country, every society,” Huang said, addressing an audience gathered online and at the iconic Dôme de Paris. From exponential inference growth to quantum breakthroughs, and from infrastructure to industry, agentic AI to robotics, Huang outlined how the region is laying the groundwork for an AI-powered future. A New Industrial Revolution At the heart of this transformation, Huang explained, are systems like GB200 NVL72 — “one giant GPU” and NVIDIA’s most powerful AI platform yet — now in full production and powering everything from sovereign models to quantum computing. “This machine was designed to be a thinking machine, a thinking machine, in the sense that it reasons, it plans, it spends a lot of time talking to itself,” Huang said, walking the audience through the size and scale of these machines and their performance. At GTC Paris, Huang showed audience members the innards of some of NVIDIA’s latest hardware. There’s more coming, with Huang saying NVIDIA’s partners are now producing 1,000 GB200 systems a week, “and this is just the beginning.” He walked the audience through a range of available systems ranging from the tiny NVIDIA DGX Spark to rack-mounted RTX PRO Servers. Huang explained that NVIDIA is working to help countries use technologies like these to build both AI infrastructure — services built for third parties to use and innovate on — and AI factories, which companies build for their own use, to generate revenue. NVIDIA is partnering with European governments, telcos and cloud providers to deploy NVIDIA technologies across the region. NVIDIA is also expanding its network of technology centers across Europe — including new hubs in Finland, Germany, Spain, Italy and the U.K. — to accelerate skills development and quantum growth. Quantum Meets Classical Europe’s quantum ambitions just got a boost. The NVIDIA CUDA-Q platform is live on Denmark’s Gefion supercomputer, opening new possibilities for hybrid AI and quantum engineering. In addition, Huang announced that CUDA-Q is now available on NVIDIA Grace Blackwell systems. Across the continent, NVIDIA is partnering with supercomputing centers and quantum hardware builders to advance hybrid quantum-AI research and accelerate quantum error correction. “Quantum computing is reaching an inflection point,” Huang said. “We are within reach of being able to apply quantum computing, quantum classical computing, in areas that can solve some interesting problems in the coming years.” Sovereign Models, Smarter Agents European developers want more control over their models. Enter NVIDIA Nemotron, designed to help build large language models tuned to local needs. “And so now you know that you have access to an enhanced open model that is still open, that is top of the leader chart,” Huang said. These models will be coming to Perplexity, a reasoning search engine, enabling secure, multilingual AI deployment across Europe. “You can now ask and get questions answered in the language, in the culture, in the sensibility of your country,” Huang said. Huang explained how NVIDIA is helping countries across Europe build AI infrastructure. Every company will build its own agents, Huang said. To help create those agents, Huang introduced a suite of agentic AI blueprints, including an Agentic AI Safety blueprint for enterprises and governments. The new NVIDIA NeMo Agent toolkit and NVIDIA AI Blueprint for building data flywheels further accelerate the development of safe, high-performing AI agents. To help deploy these agents, NVIDIA is partnering with European governments, telcos and cloud providers to deploy the DGX Cloud Lepton platform across the region, providing instant access to accelerated computing capacity. “One model architecture, one deployment, and you can run it anywhere,” Huang said, adding that Lepton is now integrated with Hugging Face, giving developers direct access to global compute. The Industrial Cloud Goes Live AI isn’t just virtual. It’s powering physical systems, too, sparking a new industrial revolution. “We’re working on industrial AI with one company after another,” Huang said, describing work to build digital twins based on the NVIDIA Omniverse platform with companies across the continent. Huang explained that everything he showed during his keynote was “computer simulation, not animation” and that it looks beautiful because “it turns out the world is beautiful, and it turns out math is beautiful.” To further this work, Huang announced NVIDIA is launching the world’s first industrial AI cloud — to be built in Germany — to help Europe’s manufacturers simulate, automate and optimize at scale. “Soon, everything that moves will be robotic,” Huang said. “And the car is the next one.” NVIDIA DRIVE, NVIDIA’s full-stack AV platform, is now in production to accelerate the large-scale deployment of safe, intelligent transportation. And to show what’s coming next, Huang was joined on stage by Grek, a pint-sized robot, as Huang talked about how NVIDIA partnered with DeepMind and Disney to build Newton, the world’s most advanced physics training engine for robotics. The Next Wave The next wave of AI has begun — and it’s exponential, Huang explained. “We have physical robots, and we have information robots. We call them agents,” Huang said. “The technology necessary to teach a robot to manipulate, to simulate — and of course, the manifestation of an incredible robot — is now right in front of us.” This new era of AI is being driven by a surge in inference workloads. “The number of people using inference has gone from 8 million to 800 million — 100x in just a couple of years,” Huang said. To meet this demand, Huang emphasized the need for a new kind of computer: “We need a special computer designed for thinking, designed for reasoning. And that’s what Blackwell is — a thinking machine.” Huang and Grek, as he explained how AI is driving advancements in robotics. These Blackwell-powered systems will live in a new class of data centers — AI factories — built to generate tokens, the raw material of modern intelligence. “These AI factories are going to generate tokens,” Huang said, turning to Grek with a smile. “And these tokens are going to become your food, little Grek.” With that, the keynote closed on a bold vision: a future powered by sovereign infrastructure, agentic AI, robotics — and exponential inference — all built in partnership with Europe. Watch the NVIDIA GTC Paris keynote from Huang at VivaTech and explore GTC Paris sessions. #nvidia #ceo #drops #blueprint #europes
    BLOGS.NVIDIA.COM
    NVIDIA CEO Drops the Blueprint for Europe’s AI Boom
    At GTC Paris — held alongside VivaTech, Europe’s largest tech event — NVIDIA founder and CEO Jensen Huang delivered a clear message: Europe isn’t just adopting AI — it’s building it. “We now have a new industry, an AI industry, and it’s now part of the new infrastructure, called intelligence infrastructure, that will be used by every country, every society,” Huang said, addressing an audience gathered online and at the iconic Dôme de Paris. From exponential inference growth to quantum breakthroughs, and from infrastructure to industry, agentic AI to robotics, Huang outlined how the region is laying the groundwork for an AI-powered future. A New Industrial Revolution At the heart of this transformation, Huang explained, are systems like GB200 NVL72 — “one giant GPU” and NVIDIA’s most powerful AI platform yet — now in full production and powering everything from sovereign models to quantum computing. “This machine was designed to be a thinking machine, a thinking machine, in the sense that it reasons, it plans, it spends a lot of time talking to itself,” Huang said, walking the audience through the size and scale of these machines and their performance. At GTC Paris, Huang showed audience members the innards of some of NVIDIA’s latest hardware. There’s more coming, with Huang saying NVIDIA’s partners are now producing 1,000 GB200 systems a week, “and this is just the beginning.” He walked the audience through a range of available systems ranging from the tiny NVIDIA DGX Spark to rack-mounted RTX PRO Servers. Huang explained that NVIDIA is working to help countries use technologies like these to build both AI infrastructure — services built for third parties to use and innovate on — and AI factories, which companies build for their own use, to generate revenue. NVIDIA is partnering with European governments, telcos and cloud providers to deploy NVIDIA technologies across the region. NVIDIA is also expanding its network of technology centers across Europe — including new hubs in Finland, Germany, Spain, Italy and the U.K. — to accelerate skills development and quantum growth. Quantum Meets Classical Europe’s quantum ambitions just got a boost. The NVIDIA CUDA-Q platform is live on Denmark’s Gefion supercomputer, opening new possibilities for hybrid AI and quantum engineering. In addition, Huang announced that CUDA-Q is now available on NVIDIA Grace Blackwell systems. Across the continent, NVIDIA is partnering with supercomputing centers and quantum hardware builders to advance hybrid quantum-AI research and accelerate quantum error correction. “Quantum computing is reaching an inflection point,” Huang said. “We are within reach of being able to apply quantum computing, quantum classical computing, in areas that can solve some interesting problems in the coming years.” Sovereign Models, Smarter Agents European developers want more control over their models. Enter NVIDIA Nemotron, designed to help build large language models tuned to local needs. “And so now you know that you have access to an enhanced open model that is still open, that is top of the leader chart,” Huang said. These models will be coming to Perplexity, a reasoning search engine, enabling secure, multilingual AI deployment across Europe. “You can now ask and get questions answered in the language, in the culture, in the sensibility of your country,” Huang said. Huang explained how NVIDIA is helping countries across Europe build AI infrastructure. Every company will build its own agents, Huang said. To help create those agents, Huang introduced a suite of agentic AI blueprints, including an Agentic AI Safety blueprint for enterprises and governments. The new NVIDIA NeMo Agent toolkit and NVIDIA AI Blueprint for building data flywheels further accelerate the development of safe, high-performing AI agents. To help deploy these agents, NVIDIA is partnering with European governments, telcos and cloud providers to deploy the DGX Cloud Lepton platform across the region, providing instant access to accelerated computing capacity. “One model architecture, one deployment, and you can run it anywhere,” Huang said, adding that Lepton is now integrated with Hugging Face, giving developers direct access to global compute. The Industrial Cloud Goes Live AI isn’t just virtual. It’s powering physical systems, too, sparking a new industrial revolution. “We’re working on industrial AI with one company after another,” Huang said, describing work to build digital twins based on the NVIDIA Omniverse platform with companies across the continent. Huang explained that everything he showed during his keynote was “computer simulation, not animation” and that it looks beautiful because “it turns out the world is beautiful, and it turns out math is beautiful.” To further this work, Huang announced NVIDIA is launching the world’s first industrial AI cloud — to be built in Germany — to help Europe’s manufacturers simulate, automate and optimize at scale. “Soon, everything that moves will be robotic,” Huang said. “And the car is the next one.” NVIDIA DRIVE, NVIDIA’s full-stack AV platform, is now in production to accelerate the large-scale deployment of safe, intelligent transportation. And to show what’s coming next, Huang was joined on stage by Grek, a pint-sized robot, as Huang talked about how NVIDIA partnered with DeepMind and Disney to build Newton, the world’s most advanced physics training engine for robotics. The Next Wave The next wave of AI has begun — and it’s exponential, Huang explained. “We have physical robots, and we have information robots. We call them agents,” Huang said. “The technology necessary to teach a robot to manipulate, to simulate — and of course, the manifestation of an incredible robot — is now right in front of us.” This new era of AI is being driven by a surge in inference workloads. “The number of people using inference has gone from 8 million to 800 million — 100x in just a couple of years,” Huang said. To meet this demand, Huang emphasized the need for a new kind of computer: “We need a special computer designed for thinking, designed for reasoning. And that’s what Blackwell is — a thinking machine.” Huang and Grek, as he explained how AI is driving advancements in robotics. These Blackwell-powered systems will live in a new class of data centers — AI factories — built to generate tokens, the raw material of modern intelligence. “These AI factories are going to generate tokens,” Huang said, turning to Grek with a smile. “And these tokens are going to become your food, little Grek.” With that, the keynote closed on a bold vision: a future powered by sovereign infrastructure, agentic AI, robotics — and exponential inference — all built in partnership with Europe. Watch the NVIDIA GTC Paris keynote from Huang at VivaTech and explore GTC Paris sessions.
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  • Plug and Play: Build a G-Assist Plug-In Today

    Project G-Assist — available through the NVIDIA App — is an experimental AI assistant that helps tune, control and optimize NVIDIA GeForce RTX systems.
    NVIDIA’s Plug and Play: Project G-Assist Plug-In Hackathon — running virtually through Wednesday, July 16 — invites the community to explore AI and build custom G-Assist plug-ins for a chance to win prizes and be featured on NVIDIA social media channels.

    G-Assist allows users to control their RTX GPU and other system settings using natural language, thanks to a small language model that runs on device. It can be used from the NVIDIA Overlay in the NVIDIA App without needing to tab out or switch programs. Users can expand its capabilities via plug-ins and even connect it to agentic frameworks such as Langflow.
    Below, find popular G-Assist plug-ins, hackathon details and tips to get started.
    Plug-In and Win
    Join the hackathon by registering and checking out the curated technical resources.
    G-Assist plug-ins can be built in several ways, including with Python for rapid development, with C++ for performance-critical apps and with custom system interactions for hardware and operating system automation.
    For those that prefer vibe coding, the G-Assist Plug-In Builder — a ChatGPT-based app that allows no-code or low-code development with natural language commands — makes it easy for enthusiasts to start creating plug-ins.
    To submit an entry, participants must provide a GitHub repository, including source code file, requirements.txt, manifest.json, config.json, a plug-in executable file and READme code.
    Then, submit a video — between 30 seconds and two minutes — showcasing the plug-in in action.
    Finally, hackathoners must promote their plug-in using #AIonRTXHackathon on a social media channel: Instagram, TikTok or X. Submit projects via this form by Wednesday, July 16.
    Judges will assess plug-ins based on three main criteria: 1) innovation and creativity, 2) technical execution and integration, reviewing technical depth, G-Assist integration and scalability, and 3) usability and community impact, aka how easy it is to use the plug-in.
    Winners will be selected on Wednesday, Aug. 20. First place will receive a GeForce RTX 5090 laptop, second place a GeForce RTX 5080 GPU and third a GeForce RTX 5070 GPU. These top three will also be featured on NVIDIA’s social media channels, get the opportunity to meet the NVIDIA G-Assist team and earn an NVIDIA Deep Learning Institute self-paced course credit.
    Project G-Assist requires a GeForce RTX 50, 40 or 30 Series Desktop GPU with at least 12GB of VRAM, Windows 11 or 10 operating system, a compatible CPU, specific disk space requirements and a recent GeForce Game Ready Driver or NVIDIA Studio Driver.
    Plug-InExplore open-source plug-in samples available on GitHub, which showcase the diverse ways on-device AI can enhance PC and gaming workflows.

    Popular plug-ins include:

    Google Gemini: Enables search-based queries using Google Search integration and large language model-based queries using Gemini capabilities in real time without needing to switch programs from the convenience of the NVIDIA App Overlay.
    Discord: Enables users to easily share game highlights or messages directly to Discord servers without disrupting gameplay.
    IFTTT: Lets users create automations across hundreds of compatible endpoints to trigger IoT routines — such as adjusting room lights and smart shades, or pushing the latest gaming news to a mobile device.
    Spotify: Lets users control Spotify using simple voice commands or the G-Assist interface to play favorite tracks and manage playlists.
    Twitch: Checks if any Twitch streamer is currently live and can access detailed stream information such as titles, games, view counts and more.

    Get G-Assist 
    Join the NVIDIA Developer Discord channel to collaborate, share creations and gain support from fellow AI enthusiasts and NVIDIA staff.
    the date for NVIDIA’s How to Build a G-Assist Plug-In webinar on Wednesday, July 9, from 10-11 a.m. PT, to learn more about Project G-Assist capabilities, discover the fundamentals of building, testing and deploying Project G-Assist plug-ins, and participate in a live Q&A session.
    Explore NVIDIA’s GitHub repository, which provides everything needed to get started developing with G-Assist, including sample plug-ins, step-by-step instructions and documentation for building custom functionalities.
    Learn more about the ChatGPT Plug-In Builder to transform ideas into functional G-Assist plug-ins with minimal coding. The tool uses OpenAI’s custom GPT builder to generate plug-in code and streamline the development process.
    NVIDIA’s technical blog walks through the architecture of a G-Assist plug-in, using a Twitch integration as an example. Discover how plug-ins work, how they communicate with G-Assist and how to build them from scratch.
    Each week, the RTX AI Garage blog series features community-driven AI innovations and content for those looking to learn more about NVIDIA NIM microservices and AI Blueprints, as well as building AI agents, creative workflows, digital humans, productivity apps and more on AI PCs and workstations. 
    Plug in to NVIDIA AI PC on Facebook, Instagram, TikTok and X — and stay informed by subscribing to the RTX AI PC newsletter.
    Follow NVIDIA Workstation on LinkedIn and X. 
    See notice regarding software product information.
    #plug #play #build #gassist #plugin
    Plug and Play: Build a G-Assist Plug-In Today
    Project G-Assist — available through the NVIDIA App — is an experimental AI assistant that helps tune, control and optimize NVIDIA GeForce RTX systems. NVIDIA’s Plug and Play: Project G-Assist Plug-In Hackathon — running virtually through Wednesday, July 16 — invites the community to explore AI and build custom G-Assist plug-ins for a chance to win prizes and be featured on NVIDIA social media channels. G-Assist allows users to control their RTX GPU and other system settings using natural language, thanks to a small language model that runs on device. It can be used from the NVIDIA Overlay in the NVIDIA App without needing to tab out or switch programs. Users can expand its capabilities via plug-ins and even connect it to agentic frameworks such as Langflow. Below, find popular G-Assist plug-ins, hackathon details and tips to get started. Plug-In and Win Join the hackathon by registering and checking out the curated technical resources. G-Assist plug-ins can be built in several ways, including with Python for rapid development, with C++ for performance-critical apps and with custom system interactions for hardware and operating system automation. For those that prefer vibe coding, the G-Assist Plug-In Builder — a ChatGPT-based app that allows no-code or low-code development with natural language commands — makes it easy for enthusiasts to start creating plug-ins. To submit an entry, participants must provide a GitHub repository, including source code file, requirements.txt, manifest.json, config.json, a plug-in executable file and READme code. Then, submit a video — between 30 seconds and two minutes — showcasing the plug-in in action. Finally, hackathoners must promote their plug-in using #AIonRTXHackathon on a social media channel: Instagram, TikTok or X. Submit projects via this form by Wednesday, July 16. Judges will assess plug-ins based on three main criteria: 1) innovation and creativity, 2) technical execution and integration, reviewing technical depth, G-Assist integration and scalability, and 3) usability and community impact, aka how easy it is to use the plug-in. Winners will be selected on Wednesday, Aug. 20. First place will receive a GeForce RTX 5090 laptop, second place a GeForce RTX 5080 GPU and third a GeForce RTX 5070 GPU. These top three will also be featured on NVIDIA’s social media channels, get the opportunity to meet the NVIDIA G-Assist team and earn an NVIDIA Deep Learning Institute self-paced course credit. Project G-Assist requires a GeForce RTX 50, 40 or 30 Series Desktop GPU with at least 12GB of VRAM, Windows 11 or 10 operating system, a compatible CPU, specific disk space requirements and a recent GeForce Game Ready Driver or NVIDIA Studio Driver. Plug-InExplore open-source plug-in samples available on GitHub, which showcase the diverse ways on-device AI can enhance PC and gaming workflows. Popular plug-ins include: Google Gemini: Enables search-based queries using Google Search integration and large language model-based queries using Gemini capabilities in real time without needing to switch programs from the convenience of the NVIDIA App Overlay. Discord: Enables users to easily share game highlights or messages directly to Discord servers without disrupting gameplay. IFTTT: Lets users create automations across hundreds of compatible endpoints to trigger IoT routines — such as adjusting room lights and smart shades, or pushing the latest gaming news to a mobile device. Spotify: Lets users control Spotify using simple voice commands or the G-Assist interface to play favorite tracks and manage playlists. Twitch: Checks if any Twitch streamer is currently live and can access detailed stream information such as titles, games, view counts and more. Get G-Assist  Join the NVIDIA Developer Discord channel to collaborate, share creations and gain support from fellow AI enthusiasts and NVIDIA staff. the date for NVIDIA’s How to Build a G-Assist Plug-In webinar on Wednesday, July 9, from 10-11 a.m. PT, to learn more about Project G-Assist capabilities, discover the fundamentals of building, testing and deploying Project G-Assist plug-ins, and participate in a live Q&A session. Explore NVIDIA’s GitHub repository, which provides everything needed to get started developing with G-Assist, including sample plug-ins, step-by-step instructions and documentation for building custom functionalities. Learn more about the ChatGPT Plug-In Builder to transform ideas into functional G-Assist plug-ins with minimal coding. The tool uses OpenAI’s custom GPT builder to generate plug-in code and streamline the development process. NVIDIA’s technical blog walks through the architecture of a G-Assist plug-in, using a Twitch integration as an example. Discover how plug-ins work, how they communicate with G-Assist and how to build them from scratch. Each week, the RTX AI Garage blog series features community-driven AI innovations and content for those looking to learn more about NVIDIA NIM microservices and AI Blueprints, as well as building AI agents, creative workflows, digital humans, productivity apps and more on AI PCs and workstations.  Plug in to NVIDIA AI PC on Facebook, Instagram, TikTok and X — and stay informed by subscribing to the RTX AI PC newsletter. Follow NVIDIA Workstation on LinkedIn and X.  See notice regarding software product information. #plug #play #build #gassist #plugin
    BLOGS.NVIDIA.COM
    Plug and Play: Build a G-Assist Plug-In Today
    Project G-Assist — available through the NVIDIA App — is an experimental AI assistant that helps tune, control and optimize NVIDIA GeForce RTX systems. NVIDIA’s Plug and Play: Project G-Assist Plug-In Hackathon — running virtually through Wednesday, July 16 — invites the community to explore AI and build custom G-Assist plug-ins for a chance to win prizes and be featured on NVIDIA social media channels. G-Assist allows users to control their RTX GPU and other system settings using natural language, thanks to a small language model that runs on device. It can be used from the NVIDIA Overlay in the NVIDIA App without needing to tab out or switch programs. Users can expand its capabilities via plug-ins and even connect it to agentic frameworks such as Langflow. Below, find popular G-Assist plug-ins, hackathon details and tips to get started. Plug-In and Win Join the hackathon by registering and checking out the curated technical resources. G-Assist plug-ins can be built in several ways, including with Python for rapid development, with C++ for performance-critical apps and with custom system interactions for hardware and operating system automation. For those that prefer vibe coding, the G-Assist Plug-In Builder — a ChatGPT-based app that allows no-code or low-code development with natural language commands — makes it easy for enthusiasts to start creating plug-ins. To submit an entry, participants must provide a GitHub repository, including source code file (plugin.py), requirements.txt, manifest.json, config.json (if applicable), a plug-in executable file and READme code. Then, submit a video — between 30 seconds and two minutes — showcasing the plug-in in action. Finally, hackathoners must promote their plug-in using #AIonRTXHackathon on a social media channel: Instagram, TikTok or X. Submit projects via this form by Wednesday, July 16. Judges will assess plug-ins based on three main criteria: 1) innovation and creativity, 2) technical execution and integration, reviewing technical depth, G-Assist integration and scalability, and 3) usability and community impact, aka how easy it is to use the plug-in. Winners will be selected on Wednesday, Aug. 20. First place will receive a GeForce RTX 5090 laptop, second place a GeForce RTX 5080 GPU and third a GeForce RTX 5070 GPU. These top three will also be featured on NVIDIA’s social media channels, get the opportunity to meet the NVIDIA G-Assist team and earn an NVIDIA Deep Learning Institute self-paced course credit. Project G-Assist requires a GeForce RTX 50, 40 or 30 Series Desktop GPU with at least 12GB of VRAM, Windows 11 or 10 operating system, a compatible CPU (Intel Pentium G Series, Core i3, i5, i7 or higher; AMD FX, Ryzen 3, 5, 7, 9, Threadripper or higher), specific disk space requirements and a recent GeForce Game Ready Driver or NVIDIA Studio Driver. Plug-In(spiration) Explore open-source plug-in samples available on GitHub, which showcase the diverse ways on-device AI can enhance PC and gaming workflows. Popular plug-ins include: Google Gemini: Enables search-based queries using Google Search integration and large language model-based queries using Gemini capabilities in real time without needing to switch programs from the convenience of the NVIDIA App Overlay. Discord: Enables users to easily share game highlights or messages directly to Discord servers without disrupting gameplay. IFTTT: Lets users create automations across hundreds of compatible endpoints to trigger IoT routines — such as adjusting room lights and smart shades, or pushing the latest gaming news to a mobile device. Spotify: Lets users control Spotify using simple voice commands or the G-Assist interface to play favorite tracks and manage playlists. Twitch: Checks if any Twitch streamer is currently live and can access detailed stream information such as titles, games, view counts and more. Get G-Assist(ance)  Join the NVIDIA Developer Discord channel to collaborate, share creations and gain support from fellow AI enthusiasts and NVIDIA staff. Save the date for NVIDIA’s How to Build a G-Assist Plug-In webinar on Wednesday, July 9, from 10-11 a.m. PT, to learn more about Project G-Assist capabilities, discover the fundamentals of building, testing and deploying Project G-Assist plug-ins, and participate in a live Q&A session. Explore NVIDIA’s GitHub repository, which provides everything needed to get started developing with G-Assist, including sample plug-ins, step-by-step instructions and documentation for building custom functionalities. Learn more about the ChatGPT Plug-In Builder to transform ideas into functional G-Assist plug-ins with minimal coding. The tool uses OpenAI’s custom GPT builder to generate plug-in code and streamline the development process. NVIDIA’s technical blog walks through the architecture of a G-Assist plug-in, using a Twitch integration as an example. Discover how plug-ins work, how they communicate with G-Assist and how to build them from scratch. Each week, the RTX AI Garage blog series features community-driven AI innovations and content for those looking to learn more about NVIDIA NIM microservices and AI Blueprints, as well as building AI agents, creative workflows, digital humans, productivity apps and more on AI PCs and workstations.  Plug in to NVIDIA AI PC on Facebook, Instagram, TikTok and X — and stay informed by subscribing to the RTX AI PC newsletter. Follow NVIDIA Workstation on LinkedIn and X.  See notice regarding software product information.
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  • HPE and NVIDIA Debut AI Factory Stack to Power Next Industrial Shift

    To speed up AI adoption across industries, HPE and NVIDIA today launched new AI factory offerings at HPE Discover in Las Vegas.
    The new lineup includes everything from modular AI factory infrastructure and HPE’s AI-ready RTX PRO Servers, to the next generation of HPE’s turnkey AI platform, HPE Private Cloud AI. The goal: give enterprises a framework to build and scale generative, agentic and industrial AI.
    The NVIDIA AI Computing by HPE portfolio is now among the broadest in the market.
    The portfolio combines NVIDIA Blackwell accelerated computing, NVIDIA Spectrum-X Ethernet and NVIDIA BlueField-3 networking technologies, NVIDIA AI Enterprise software and HPE’s full portfolio of servers, storage, services and software. This now includes HPE OpsRamp Software, a validated observability solution for the NVIDIA Enterprise AI Factory, and HPE Morpheus Enterprise Software for orchestration. The result is a pre-integrated, modular infrastructure stack to help teams get AI into production faster.
    This includes the next-generation HPE Private Cloud AI, co-engineered with NVIDIA and validated as part of the NVIDIA Enterprise AI Factory framework. This full-stack, turnkey AI factory solution will offer HPE ProLiant Compute DL380a Gen12 servers with the new NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs.
    These new NVIDIA RTX PRO Servers from HPE provide a universal data center platform for a wide range of enterprise AI and industrial AI use cases, and are now available to order from HPE. HPE Private Cloud AI includes the latest NVIDIA AI Blueprints, including the NVIDIA AI-Q Blueprint for AI agent creation and workflows.
    HPE also announced a new NVIDIA HGX B300 system, the HPE Compute XD690, built with NVIDIA Blackwell Ultra GPUs. It’s the latest entry in the NVIDIA AI Computing by HPE lineup and is expected to ship in October.
    In Japan, KDDI is working with HPE to build NVIDIA AI infrastructure to accelerate global adoption.
    The HPE-built KDDI system will be based on the NVIDIA GB200 NVL72 platform, built on the NVIDIA Grace Blackwell architecture, at the KDDI Osaka Sakai Data Center.
    To accelerate AI for financial services, HPE will co-test agentic AI workflows built on Accenture’s AI Refinery with NVIDIA, running on HPE Private Cloud AI. Initial use cases include sourcing, procurement and risk analysis.
    HPE said it’s adding 26 new partners to its “Unleash AI” ecosystem to support more NVIDIA AI use cases. The company now offers more than 70 packaged AI workloads, from fraud detection and video analytics to sovereign AI and cybersecurity.
    Security and governance were a focus, too. HPE Private Cloud AI supports air-gapped management, multi-tenancy and post-quantum cryptography. HPE’s try-before-you-buy program lets customers test the system in Equinix data centers before purchase. HPE also introduced new programs, including AI Acceleration Workshops with NVIDIA, to help scale AI deployments.

    Watch the keynote: HPE CEO Antonio Neri announced the news from the Las Vegas Sphere on Tuesday at 9 a.m. PT. Register for the livestream and watch the replay.
    Explore more: Learn how NVIDIA and HPE build AI factories for every industry. Visit the partner page.
    #hpe #nvidia #debut #factory #stack
    HPE and NVIDIA Debut AI Factory Stack to Power Next Industrial Shift
    To speed up AI adoption across industries, HPE and NVIDIA today launched new AI factory offerings at HPE Discover in Las Vegas. The new lineup includes everything from modular AI factory infrastructure and HPE’s AI-ready RTX PRO Servers, to the next generation of HPE’s turnkey AI platform, HPE Private Cloud AI. The goal: give enterprises a framework to build and scale generative, agentic and industrial AI. The NVIDIA AI Computing by HPE portfolio is now among the broadest in the market. The portfolio combines NVIDIA Blackwell accelerated computing, NVIDIA Spectrum-X Ethernet and NVIDIA BlueField-3 networking technologies, NVIDIA AI Enterprise software and HPE’s full portfolio of servers, storage, services and software. This now includes HPE OpsRamp Software, a validated observability solution for the NVIDIA Enterprise AI Factory, and HPE Morpheus Enterprise Software for orchestration. The result is a pre-integrated, modular infrastructure stack to help teams get AI into production faster. This includes the next-generation HPE Private Cloud AI, co-engineered with NVIDIA and validated as part of the NVIDIA Enterprise AI Factory framework. This full-stack, turnkey AI factory solution will offer HPE ProLiant Compute DL380a Gen12 servers with the new NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs. These new NVIDIA RTX PRO Servers from HPE provide a universal data center platform for a wide range of enterprise AI and industrial AI use cases, and are now available to order from HPE. HPE Private Cloud AI includes the latest NVIDIA AI Blueprints, including the NVIDIA AI-Q Blueprint for AI agent creation and workflows. HPE also announced a new NVIDIA HGX B300 system, the HPE Compute XD690, built with NVIDIA Blackwell Ultra GPUs. It’s the latest entry in the NVIDIA AI Computing by HPE lineup and is expected to ship in October. In Japan, KDDI is working with HPE to build NVIDIA AI infrastructure to accelerate global adoption. The HPE-built KDDI system will be based on the NVIDIA GB200 NVL72 platform, built on the NVIDIA Grace Blackwell architecture, at the KDDI Osaka Sakai Data Center. To accelerate AI for financial services, HPE will co-test agentic AI workflows built on Accenture’s AI Refinery with NVIDIA, running on HPE Private Cloud AI. Initial use cases include sourcing, procurement and risk analysis. HPE said it’s adding 26 new partners to its “Unleash AI” ecosystem to support more NVIDIA AI use cases. The company now offers more than 70 packaged AI workloads, from fraud detection and video analytics to sovereign AI and cybersecurity. Security and governance were a focus, too. HPE Private Cloud AI supports air-gapped management, multi-tenancy and post-quantum cryptography. HPE’s try-before-you-buy program lets customers test the system in Equinix data centers before purchase. HPE also introduced new programs, including AI Acceleration Workshops with NVIDIA, to help scale AI deployments. Watch the keynote: HPE CEO Antonio Neri announced the news from the Las Vegas Sphere on Tuesday at 9 a.m. PT. Register for the livestream and watch the replay. Explore more: Learn how NVIDIA and HPE build AI factories for every industry. Visit the partner page. #hpe #nvidia #debut #factory #stack
    BLOGS.NVIDIA.COM
    HPE and NVIDIA Debut AI Factory Stack to Power Next Industrial Shift
    To speed up AI adoption across industries, HPE and NVIDIA today launched new AI factory offerings at HPE Discover in Las Vegas. The new lineup includes everything from modular AI factory infrastructure and HPE’s AI-ready RTX PRO Servers (HPE ProLiant Compute DL380a Gen12), to the next generation of HPE’s turnkey AI platform, HPE Private Cloud AI. The goal: give enterprises a framework to build and scale generative, agentic and industrial AI. The NVIDIA AI Computing by HPE portfolio is now among the broadest in the market. The portfolio combines NVIDIA Blackwell accelerated computing, NVIDIA Spectrum-X Ethernet and NVIDIA BlueField-3 networking technologies, NVIDIA AI Enterprise software and HPE’s full portfolio of servers, storage, services and software. This now includes HPE OpsRamp Software, a validated observability solution for the NVIDIA Enterprise AI Factory, and HPE Morpheus Enterprise Software for orchestration. The result is a pre-integrated, modular infrastructure stack to help teams get AI into production faster. This includes the next-generation HPE Private Cloud AI, co-engineered with NVIDIA and validated as part of the NVIDIA Enterprise AI Factory framework. This full-stack, turnkey AI factory solution will offer HPE ProLiant Compute DL380a Gen12 servers with the new NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs. These new NVIDIA RTX PRO Servers from HPE provide a universal data center platform for a wide range of enterprise AI and industrial AI use cases, and are now available to order from HPE. HPE Private Cloud AI includes the latest NVIDIA AI Blueprints, including the NVIDIA AI-Q Blueprint for AI agent creation and workflows. HPE also announced a new NVIDIA HGX B300 system, the HPE Compute XD690, built with NVIDIA Blackwell Ultra GPUs. It’s the latest entry in the NVIDIA AI Computing by HPE lineup and is expected to ship in October. In Japan, KDDI is working with HPE to build NVIDIA AI infrastructure to accelerate global adoption. The HPE-built KDDI system will be based on the NVIDIA GB200 NVL72 platform, built on the NVIDIA Grace Blackwell architecture, at the KDDI Osaka Sakai Data Center. To accelerate AI for financial services, HPE will co-test agentic AI workflows built on Accenture’s AI Refinery with NVIDIA, running on HPE Private Cloud AI. Initial use cases include sourcing, procurement and risk analysis. HPE said it’s adding 26 new partners to its “Unleash AI” ecosystem to support more NVIDIA AI use cases. The company now offers more than 70 packaged AI workloads, from fraud detection and video analytics to sovereign AI and cybersecurity. Security and governance were a focus, too. HPE Private Cloud AI supports air-gapped management, multi-tenancy and post-quantum cryptography. HPE’s try-before-you-buy program lets customers test the system in Equinix data centers before purchase. HPE also introduced new programs, including AI Acceleration Workshops with NVIDIA, to help scale AI deployments. Watch the keynote: HPE CEO Antonio Neri announced the news from the Las Vegas Sphere on Tuesday at 9 a.m. PT. Register for the livestream and watch the replay. Explore more: Learn how NVIDIA and HPE build AI factories for every industry. Visit the partner page.
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  • Startup Uses NVIDIA RTX-Powered Generative AI to Make Coolers, Cooler

    Mark Theriault founded the startup FITY envisioning a line of clever cooling products: cold drink holders that come with freezable pucks to keep beverages cold for longer without the mess of ice. The entrepreneur started with 3D prints of products in his basement, building one unit at a time, before eventually scaling to mass production.
    Founding a consumer product company from scratch was a tall order for a single person. Going from preliminary sketches to production-ready designs was a major challenge. To bring his creative vision to life, Theriault relied on AI and his NVIDIA GeForce RTX-equipped system. For him, AI isn’t just a tool — it’s an entire pipeline to help him accomplish his goals. about his workflow below.
    Plus, GeForce RTX 5050 laptops start arriving today at retailers worldwide, from GeForce RTX 5050 Laptop GPUs feature 2,560 NVIDIA Blackwell CUDA cores, fifth-generation AI Tensor Cores, fourth-generation RT Cores, a ninth-generation NVENC encoder and a sixth-generation NVDEC decoder.
    In addition, NVIDIA’s Plug and Play: Project G-Assist Plug-In Hackathon — running virtually through Wednesday, July 16 — invites developers to explore AI and build custom G-Assist plug-ins for a chance to win prizes. the date for the G-Assist Plug-In webinar on Wednesday, July 9, from 10-11 a.m. PT, to learn more about Project G-Assist capabilities and fundamentals, and to participate in a live Q&A session.
    From Concept to Completion
    To create his standout products, Theriault tinkers with potential FITY Flex cooler designs with traditional methods, from sketch to computer-aided design to rapid prototyping, until he finds the right vision. A unique aspect of the FITY Flex design is that it can be customized with fun, popular shoe charms.
    For packaging design inspiration, Theriault uses his preferred text-to-image generative AI model for prototyping, Stable Diffusion XL — which runs 60% faster with the NVIDIA TensorRT software development kit — using the modular, node-based interface ComfyUI.
    ComfyUI gives users granular control over every step of the generation process — prompting, sampling, model loading, image conditioning and post-processing. It’s ideal for advanced users like Theriault who want to customize how images are generated.
    Theriault’s uses of AI result in a complete computer graphics-based ad campaign. Image courtesy of FITY.
    NVIDIA and GeForce RTX GPUs based on the NVIDIA Blackwell architecture include fifth-generation Tensor Cores designed to accelerate AI and deep learning workloads. These GPUs work with CUDA optimizations in PyTorch to seamlessly accelerate ComfyUI, reducing generation time on FLUX.1-dev, an image generation model from Black Forest Labs, from two minutes per image on the Mac M3 Ultra to about four seconds on the GeForce RTX 5090 desktop GPU.
    ComfyUI can also add ControlNets — AI models that help control image generation — that Theriault uses for tasks like guiding human poses, setting compositions via depth mapping and converting scribbles to images.
    Theriault even creates his own fine-tuned models to keep his style consistent. He used low-rank adaptationmodels — small, efficient adapters into specific layers of the network — enabling hyper-customized generation with minimal compute cost.
    LoRA models allow Theriault to ideate on visuals quickly. Image courtesy of FITY.
    “Over the last few months, I’ve been shifting from AI-assisted computer graphics renders to fully AI-generated product imagery using a custom Flux LoRA I trained in house. My RTX 4080 SUPER GPU has been essential for getting the performance I need to train and iterate quickly.” – Mark Theriault, founder of FITY 

    Theriault also taps into generative AI to create marketing assets like FITY Flex product packaging. He uses FLUX.1, which excels at generating legible text within images, addressing a common challenge in text-to-image models.
    Though FLUX.1 models can typically consume over 23GB of VRAM, NVIDIA has collaborated with Black Forest Labs to help reduce the size of these models using quantization — a technique that reduces model size while maintaining quality. The models were then accelerated with TensorRT, which provides an up to 2x speedup over PyTorch.
    To simplify using these models in ComfyUI, NVIDIA created the FLUX.1 NIM microservice, a containerized version of FLUX.1 that can be loaded in ComfyUI and enables FP4 quantization and TensorRT support. Combined, the models come down to just over 11GB of VRAM, and performance improves by 2.5x.
    Theriault uses the Blender Cycles app to render out final files. For 3D workflows, NVIDIA offers the AI Blueprint for 3D-guided generative AI to ease the positioning and composition of 3D images, so anyone interested in this method can quickly get started.
    Photorealistic renders. Image courtesy of FITY.
    Finally, Theriault uses large language models to generate marketing copy — tailored for search engine optimization, tone and storytelling — as well as to complete his patent and provisional applications, work that usually costs thousands of dollars in legal fees and considerable time.
    Generative AI helps Theriault create promotional materials like the above. Image courtesy of FITY.
    “As a one-man band with a ton of content to generate, having on-the-fly generation capabilities for my product designs really helps speed things up.” – Mark Theriault, founder of FITY

    Every texture, every word, every photo, every accessory was a micro-decision, Theriault said. AI helped him survive the “death by a thousand cuts” that can stall solo startup founders, he added.
    Each week, the RTX AI Garage blog series features community-driven AI innovations and content for those looking to learn more about NVIDIA NIM microservices and AI Blueprints, as well as building AI agents, creative workflows, digital humans, productivity apps and more on AI PCs and workstations. 
    Plug in to NVIDIA AI PC on Facebook, Instagram, TikTok and X — and stay informed by subscribing to the RTX AI PC newsletter.
    Follow NVIDIA Workstation on LinkedIn and X. 
    See notice regarding software product information.
    #startup #uses #nvidia #rtxpowered #generative
    Startup Uses NVIDIA RTX-Powered Generative AI to Make Coolers, Cooler
    Mark Theriault founded the startup FITY envisioning a line of clever cooling products: cold drink holders that come with freezable pucks to keep beverages cold for longer without the mess of ice. The entrepreneur started with 3D prints of products in his basement, building one unit at a time, before eventually scaling to mass production. Founding a consumer product company from scratch was a tall order for a single person. Going from preliminary sketches to production-ready designs was a major challenge. To bring his creative vision to life, Theriault relied on AI and his NVIDIA GeForce RTX-equipped system. For him, AI isn’t just a tool — it’s an entire pipeline to help him accomplish his goals. about his workflow below. Plus, GeForce RTX 5050 laptops start arriving today at retailers worldwide, from GeForce RTX 5050 Laptop GPUs feature 2,560 NVIDIA Blackwell CUDA cores, fifth-generation AI Tensor Cores, fourth-generation RT Cores, a ninth-generation NVENC encoder and a sixth-generation NVDEC decoder. In addition, NVIDIA’s Plug and Play: Project G-Assist Plug-In Hackathon — running virtually through Wednesday, July 16 — invites developers to explore AI and build custom G-Assist plug-ins for a chance to win prizes. the date for the G-Assist Plug-In webinar on Wednesday, July 9, from 10-11 a.m. PT, to learn more about Project G-Assist capabilities and fundamentals, and to participate in a live Q&A session. From Concept to Completion To create his standout products, Theriault tinkers with potential FITY Flex cooler designs with traditional methods, from sketch to computer-aided design to rapid prototyping, until he finds the right vision. A unique aspect of the FITY Flex design is that it can be customized with fun, popular shoe charms. For packaging design inspiration, Theriault uses his preferred text-to-image generative AI model for prototyping, Stable Diffusion XL — which runs 60% faster with the NVIDIA TensorRT software development kit — using the modular, node-based interface ComfyUI. ComfyUI gives users granular control over every step of the generation process — prompting, sampling, model loading, image conditioning and post-processing. It’s ideal for advanced users like Theriault who want to customize how images are generated. Theriault’s uses of AI result in a complete computer graphics-based ad campaign. Image courtesy of FITY. NVIDIA and GeForce RTX GPUs based on the NVIDIA Blackwell architecture include fifth-generation Tensor Cores designed to accelerate AI and deep learning workloads. These GPUs work with CUDA optimizations in PyTorch to seamlessly accelerate ComfyUI, reducing generation time on FLUX.1-dev, an image generation model from Black Forest Labs, from two minutes per image on the Mac M3 Ultra to about four seconds on the GeForce RTX 5090 desktop GPU. ComfyUI can also add ControlNets — AI models that help control image generation — that Theriault uses for tasks like guiding human poses, setting compositions via depth mapping and converting scribbles to images. Theriault even creates his own fine-tuned models to keep his style consistent. He used low-rank adaptationmodels — small, efficient adapters into specific layers of the network — enabling hyper-customized generation with minimal compute cost. LoRA models allow Theriault to ideate on visuals quickly. Image courtesy of FITY. “Over the last few months, I’ve been shifting from AI-assisted computer graphics renders to fully AI-generated product imagery using a custom Flux LoRA I trained in house. My RTX 4080 SUPER GPU has been essential for getting the performance I need to train and iterate quickly.” – Mark Theriault, founder of FITY  Theriault also taps into generative AI to create marketing assets like FITY Flex product packaging. He uses FLUX.1, which excels at generating legible text within images, addressing a common challenge in text-to-image models. Though FLUX.1 models can typically consume over 23GB of VRAM, NVIDIA has collaborated with Black Forest Labs to help reduce the size of these models using quantization — a technique that reduces model size while maintaining quality. The models were then accelerated with TensorRT, which provides an up to 2x speedup over PyTorch. To simplify using these models in ComfyUI, NVIDIA created the FLUX.1 NIM microservice, a containerized version of FLUX.1 that can be loaded in ComfyUI and enables FP4 quantization and TensorRT support. Combined, the models come down to just over 11GB of VRAM, and performance improves by 2.5x. Theriault uses the Blender Cycles app to render out final files. For 3D workflows, NVIDIA offers the AI Blueprint for 3D-guided generative AI to ease the positioning and composition of 3D images, so anyone interested in this method can quickly get started. Photorealistic renders. Image courtesy of FITY. Finally, Theriault uses large language models to generate marketing copy — tailored for search engine optimization, tone and storytelling — as well as to complete his patent and provisional applications, work that usually costs thousands of dollars in legal fees and considerable time. Generative AI helps Theriault create promotional materials like the above. Image courtesy of FITY. “As a one-man band with a ton of content to generate, having on-the-fly generation capabilities for my product designs really helps speed things up.” – Mark Theriault, founder of FITY Every texture, every word, every photo, every accessory was a micro-decision, Theriault said. AI helped him survive the “death by a thousand cuts” that can stall solo startup founders, he added. Each week, the RTX AI Garage blog series features community-driven AI innovations and content for those looking to learn more about NVIDIA NIM microservices and AI Blueprints, as well as building AI agents, creative workflows, digital humans, productivity apps and more on AI PCs and workstations.  Plug in to NVIDIA AI PC on Facebook, Instagram, TikTok and X — and stay informed by subscribing to the RTX AI PC newsletter. Follow NVIDIA Workstation on LinkedIn and X.  See notice regarding software product information. #startup #uses #nvidia #rtxpowered #generative
    BLOGS.NVIDIA.COM
    Startup Uses NVIDIA RTX-Powered Generative AI to Make Coolers, Cooler
    Mark Theriault founded the startup FITY envisioning a line of clever cooling products: cold drink holders that come with freezable pucks to keep beverages cold for longer without the mess of ice. The entrepreneur started with 3D prints of products in his basement, building one unit at a time, before eventually scaling to mass production. Founding a consumer product company from scratch was a tall order for a single person. Going from preliminary sketches to production-ready designs was a major challenge. To bring his creative vision to life, Theriault relied on AI and his NVIDIA GeForce RTX-equipped system. For him, AI isn’t just a tool — it’s an entire pipeline to help him accomplish his goals. Read more about his workflow below. Plus, GeForce RTX 5050 laptops start arriving today at retailers worldwide, from $999. GeForce RTX 5050 Laptop GPUs feature 2,560 NVIDIA Blackwell CUDA cores, fifth-generation AI Tensor Cores, fourth-generation RT Cores, a ninth-generation NVENC encoder and a sixth-generation NVDEC decoder. In addition, NVIDIA’s Plug and Play: Project G-Assist Plug-In Hackathon — running virtually through Wednesday, July 16 — invites developers to explore AI and build custom G-Assist plug-ins for a chance to win prizes. Save the date for the G-Assist Plug-In webinar on Wednesday, July 9, from 10-11 a.m. PT, to learn more about Project G-Assist capabilities and fundamentals, and to participate in a live Q&A session. From Concept to Completion To create his standout products, Theriault tinkers with potential FITY Flex cooler designs with traditional methods, from sketch to computer-aided design to rapid prototyping, until he finds the right vision. A unique aspect of the FITY Flex design is that it can be customized with fun, popular shoe charms. For packaging design inspiration, Theriault uses his preferred text-to-image generative AI model for prototyping, Stable Diffusion XL — which runs 60% faster with the NVIDIA TensorRT software development kit — using the modular, node-based interface ComfyUI. ComfyUI gives users granular control over every step of the generation process — prompting, sampling, model loading, image conditioning and post-processing. It’s ideal for advanced users like Theriault who want to customize how images are generated. Theriault’s uses of AI result in a complete computer graphics-based ad campaign. Image courtesy of FITY. NVIDIA and GeForce RTX GPUs based on the NVIDIA Blackwell architecture include fifth-generation Tensor Cores designed to accelerate AI and deep learning workloads. These GPUs work with CUDA optimizations in PyTorch to seamlessly accelerate ComfyUI, reducing generation time on FLUX.1-dev, an image generation model from Black Forest Labs, from two minutes per image on the Mac M3 Ultra to about four seconds on the GeForce RTX 5090 desktop GPU. ComfyUI can also add ControlNets — AI models that help control image generation — that Theriault uses for tasks like guiding human poses, setting compositions via depth mapping and converting scribbles to images. Theriault even creates his own fine-tuned models to keep his style consistent. He used low-rank adaptation (LoRA) models — small, efficient adapters into specific layers of the network — enabling hyper-customized generation with minimal compute cost. LoRA models allow Theriault to ideate on visuals quickly. Image courtesy of FITY. “Over the last few months, I’ve been shifting from AI-assisted computer graphics renders to fully AI-generated product imagery using a custom Flux LoRA I trained in house. My RTX 4080 SUPER GPU has been essential for getting the performance I need to train and iterate quickly.” – Mark Theriault, founder of FITY  Theriault also taps into generative AI to create marketing assets like FITY Flex product packaging. He uses FLUX.1, which excels at generating legible text within images, addressing a common challenge in text-to-image models. Though FLUX.1 models can typically consume over 23GB of VRAM, NVIDIA has collaborated with Black Forest Labs to help reduce the size of these models using quantization — a technique that reduces model size while maintaining quality. The models were then accelerated with TensorRT, which provides an up to 2x speedup over PyTorch. To simplify using these models in ComfyUI, NVIDIA created the FLUX.1 NIM microservice, a containerized version of FLUX.1 that can be loaded in ComfyUI and enables FP4 quantization and TensorRT support. Combined, the models come down to just over 11GB of VRAM, and performance improves by 2.5x. Theriault uses the Blender Cycles app to render out final files. For 3D workflows, NVIDIA offers the AI Blueprint for 3D-guided generative AI to ease the positioning and composition of 3D images, so anyone interested in this method can quickly get started. Photorealistic renders. Image courtesy of FITY. Finally, Theriault uses large language models to generate marketing copy — tailored for search engine optimization, tone and storytelling — as well as to complete his patent and provisional applications, work that usually costs thousands of dollars in legal fees and considerable time. Generative AI helps Theriault create promotional materials like the above. Image courtesy of FITY. “As a one-man band with a ton of content to generate, having on-the-fly generation capabilities for my product designs really helps speed things up.” – Mark Theriault, founder of FITY Every texture, every word, every photo, every accessory was a micro-decision, Theriault said. AI helped him survive the “death by a thousand cuts” that can stall solo startup founders, he added. Each week, the RTX AI Garage blog series features community-driven AI innovations and content for those looking to learn more about NVIDIA NIM microservices and AI Blueprints, as well as building AI agents, creative workflows, digital humans, productivity apps and more on AI PCs and workstations.  Plug in to NVIDIA AI PC on Facebook, Instagram, TikTok and X — and stay informed by subscribing to the RTX AI PC newsletter. Follow NVIDIA Workstation on LinkedIn and X.  See notice regarding software product information.
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  • In a world where open-source AI thrives on hope and collaboration, I often find myself lost in a sea of expectations and overwhelming complexities. Every line of code feels like a reminder of the countless hours I pour into trying to keep up with the ever-evolving landscape. "It’s hard," I whisper to myself, as the weight of my solitude presses down.

    Blueprints meant to simplify this journey often seem like distant dreams, slipping through my fingers just when I think I've grasped the essence of what they promise. It's hard to watch as others seem to navigate the waters of integration and experimentation with ease, while I flounder, overwhelmed by poorly maintained libraries and breaking compatibility with every update. I want to create, to experiment quickly, but the barriers are suffocating, leaving me to question my place in this vast, technological expanse.

    I sit for hours, my screen illuminating a path that feels both familiar and foreign. Frustration bubbles beneath the surface—why is it that the very tools designed to foster creativity can also ensnare us in confusion? Each failed attempt is a dagger to my spirit, reminding me of the isolation I feel in a community that should be united. I watch, I learn, but the connection fades, leaving me in shadows where the light of collaboration once shone brightly.

    Every project I undertake feels like a solitary expedition into the unknown. I crave the camaraderie of fellow explorers, yet here I am, navigating this labyrinth alone. The promise of open-source AI is a beacon of hope, but the realization of its challenges often feels like a cruel joke. The freedom to create is entangled with the chains of necessity—a bitter irony that leaves me feeling more isolated than ever.

    I long for moments of clarity, for those blueprints to unfurl like sails catching the wind, propelling me forward into a landscape where creativity flows freely and innovation knows no bounds. But with each passing day, the struggle continues, a reminder that though the journey is meant to be shared, I often find myself standing at the precipice, staring into the abyss of my own doubts and fears.

    In this digital age, I hold onto the glimmers of hope that maybe, just maybe, the community will rise together to confront these challenges. But until then, I mourn the connections lost and the dreams that fade with each failed integration. The burden of loneliness is heavy, yet I carry it, hoping that one day it will transform into the wings of liberation I so desperately seek.

    #OpenSourceAI #Loneliness #Creativity #IntegrationChallenges #Blueprints
    In a world where open-source AI thrives on hope and collaboration, I often find myself lost in a sea of expectations and overwhelming complexities. 💔 Every line of code feels like a reminder of the countless hours I pour into trying to keep up with the ever-evolving landscape. "It’s hard," I whisper to myself, as the weight of my solitude presses down. Blueprints meant to simplify this journey often seem like distant dreams, slipping through my fingers just when I think I've grasped the essence of what they promise. It's hard to watch as others seem to navigate the waters of integration and experimentation with ease, while I flounder, overwhelmed by poorly maintained libraries and breaking compatibility with every update. I want to create, to experiment quickly, but the barriers are suffocating, leaving me to question my place in this vast, technological expanse. 🤖 I sit for hours, my screen illuminating a path that feels both familiar and foreign. Frustration bubbles beneath the surface—why is it that the very tools designed to foster creativity can also ensnare us in confusion? Each failed attempt is a dagger to my spirit, reminding me of the isolation I feel in a community that should be united. I watch, I learn, but the connection fades, leaving me in shadows where the light of collaboration once shone brightly. Every project I undertake feels like a solitary expedition into the unknown. I crave the camaraderie of fellow explorers, yet here I am, navigating this labyrinth alone. The promise of open-source AI is a beacon of hope, but the realization of its challenges often feels like a cruel joke. The freedom to create is entangled with the chains of necessity—a bitter irony that leaves me feeling more isolated than ever. I long for moments of clarity, for those blueprints to unfurl like sails catching the wind, propelling me forward into a landscape where creativity flows freely and innovation knows no bounds. But with each passing day, the struggle continues, a reminder that though the journey is meant to be shared, I often find myself standing at the precipice, staring into the abyss of my own doubts and fears. In this digital age, I hold onto the glimmers of hope that maybe, just maybe, the community will rise together to confront these challenges. But until then, I mourn the connections lost and the dreams that fade with each failed integration. The burden of loneliness is heavy, yet I carry it, hoping that one day it will transform into the wings of liberation I so desperately seek. 🌌 #OpenSourceAI #Loneliness #Creativity #IntegrationChallenges #Blueprints
    Open-source AI is hard. Blueprints can help!
    “I spend 8 hours per week trying to keep up to date, it’s overwhelming!” “Integrating new libraries is difficult. They’re either poorly maintained or updated in ways that break compatibility.” “I want to be able to experiment quickly, without r
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  • NVIDIA TensorRT Boosts Stable Diffusion 3.5 Performance on NVIDIA GeForce RTX and RTX PRO GPUs

    Generative AI has reshaped how people create, imagine and interact with digital content.
    As AI models continue to grow in capability and complexity, they require more VRAM, or video random access memory. The base Stable Diffusion 3.5 Large model, for example, uses over 18GB of VRAM — limiting the number of systems that can run it well.
    By applying quantization to the model, noncritical layers can be removed or run with lower precision. NVIDIA GeForce RTX 40 Series and the Ada Lovelace generation of NVIDIA RTX PRO GPUs support FP8 quantization to help run these quantized models, and the latest-generation NVIDIA Blackwell GPUs also add support for FP4.
    NVIDIA collaborated with Stability AI to quantize its latest model, Stable Diffusion3.5 Large, to FP8 — reducing VRAM consumption by 40%. Further optimizations to SD3.5 Large and Medium with the NVIDIA TensorRT software development kitdouble performance.
    In addition, TensorRT has been reimagined for RTX AI PCs, combining its industry-leading performance with just-in-time, on-device engine building and an 8x smaller package size for seamless AI deployment to more than 100 million RTX AI PCs. TensorRT for RTX is now available as a standalone SDK for developers.
    RTX-Accelerated AI
    NVIDIA and Stability AI are boosting the performance and reducing the VRAM requirements of Stable Diffusion 3.5, one of the world’s most popular AI image models. With NVIDIA TensorRT acceleration and quantization, users can now generate and edit images faster and more efficiently on NVIDIA RTX GPUs.
    Stable Diffusion 3.5 quantized FP8generates images in half the time with similar quality as FP16. Prompt: A serene mountain lake at sunrise, crystal clear water reflecting snow-capped peaks, lush pine trees along the shore, soft morning mist, photorealistic, vibrant colors, high resolution.
    To address the VRAM limitations of SD3.5 Large, the model was quantized with TensorRT to FP8, reducing the VRAM requirement by 40% to 11GB. This means five GeForce RTX 50 Series GPUs can run the model from memory instead of just one.
    SD3.5 Large and Medium models were also optimized with TensorRT, an AI backend for taking full advantage of Tensor Cores. TensorRT optimizes a model’s weights and graph — the instructions on how to run a model — specifically for RTX GPUs.
    FP8 TensorRT boosts SD3.5 Large performance by 2.3x vs. BF16 PyTorch, with 40% less memory use. For SD3.5 Medium, BF16 TensorRT delivers a 1.7x speedup.
    Combined, FP8 TensorRT delivers a 2.3x performance boost on SD3.5 Large compared with running the original models in BF16 PyTorch, while using 40% less memory. And in SD3.5 Medium, BF16 TensorRT provides a 1.7x performance increase compared with BF16 PyTorch.
    The optimized models are now available on Stability AI’s Hugging Face page.
    NVIDIA and Stability AI are also collaborating to release SD3.5 as an NVIDIA NIM microservice, making it easier for creators and developers to access and deploy the model for a wide range of applications. The NIM microservice is expected to be released in July.
    TensorRT for RTX SDK Released
    Announced at Microsoft Build — and already available as part of the new Windows ML framework in preview — TensorRT for RTX is now available as a standalone SDK for developers.
    Previously, developers needed to pre-generate and package TensorRT engines for each class of GPU — a process that would yield GPU-specific optimizations but required significant time.
    With the new version of TensorRT, developers can create a generic TensorRT engine that’s optimized on device in seconds. This JIT compilation approach can be done in the background during installation or when they first use the feature.
    The easy-to-integrate SDK is now 8x smaller and can be invoked through Windows ML — Microsoft’s new AI inference backend in Windows. Developers can download the new standalone SDK from the NVIDIA Developer page or test it in the Windows ML preview.
    For more details, read this NVIDIA technical blog and this Microsoft Build recap.
    Join NVIDIA at GTC Paris
    At NVIDIA GTC Paris at VivaTech — Europe’s biggest startup and tech event — NVIDIA founder and CEO Jensen Huang yesterday delivered a keynote address on the latest breakthroughs in cloud AI infrastructure, agentic AI and physical AI. Watch a replay.
    GTC Paris runs through Thursday, June 12, with hands-on demos and sessions led by industry leaders. Whether attending in person or joining online, there’s still plenty to explore at the event.
    Each week, the RTX AI Garage blog series features community-driven AI innovations and content for those looking to learn more about NVIDIA NIM microservices and AI Blueprints, as well as building AI agents, creative workflows, digital humans, productivity apps and more on AI PCs and workstations. 
    Plug in to NVIDIA AI PC on Facebook, Instagram, TikTok and X — and stay informed by subscribing to the RTX AI PC newsletter.
    Follow NVIDIA Workstation on LinkedIn and X. 
    See notice regarding software product information.
    #nvidia #tensorrt #boosts #stable #diffusion
    NVIDIA TensorRT Boosts Stable Diffusion 3.5 Performance on NVIDIA GeForce RTX and RTX PRO GPUs
    Generative AI has reshaped how people create, imagine and interact with digital content. As AI models continue to grow in capability and complexity, they require more VRAM, or video random access memory. The base Stable Diffusion 3.5 Large model, for example, uses over 18GB of VRAM — limiting the number of systems that can run it well. By applying quantization to the model, noncritical layers can be removed or run with lower precision. NVIDIA GeForce RTX 40 Series and the Ada Lovelace generation of NVIDIA RTX PRO GPUs support FP8 quantization to help run these quantized models, and the latest-generation NVIDIA Blackwell GPUs also add support for FP4. NVIDIA collaborated with Stability AI to quantize its latest model, Stable Diffusion3.5 Large, to FP8 — reducing VRAM consumption by 40%. Further optimizations to SD3.5 Large and Medium with the NVIDIA TensorRT software development kitdouble performance. In addition, TensorRT has been reimagined for RTX AI PCs, combining its industry-leading performance with just-in-time, on-device engine building and an 8x smaller package size for seamless AI deployment to more than 100 million RTX AI PCs. TensorRT for RTX is now available as a standalone SDK for developers. RTX-Accelerated AI NVIDIA and Stability AI are boosting the performance and reducing the VRAM requirements of Stable Diffusion 3.5, one of the world’s most popular AI image models. With NVIDIA TensorRT acceleration and quantization, users can now generate and edit images faster and more efficiently on NVIDIA RTX GPUs. Stable Diffusion 3.5 quantized FP8generates images in half the time with similar quality as FP16. Prompt: A serene mountain lake at sunrise, crystal clear water reflecting snow-capped peaks, lush pine trees along the shore, soft morning mist, photorealistic, vibrant colors, high resolution. To address the VRAM limitations of SD3.5 Large, the model was quantized with TensorRT to FP8, reducing the VRAM requirement by 40% to 11GB. This means five GeForce RTX 50 Series GPUs can run the model from memory instead of just one. SD3.5 Large and Medium models were also optimized with TensorRT, an AI backend for taking full advantage of Tensor Cores. TensorRT optimizes a model’s weights and graph — the instructions on how to run a model — specifically for RTX GPUs. FP8 TensorRT boosts SD3.5 Large performance by 2.3x vs. BF16 PyTorch, with 40% less memory use. For SD3.5 Medium, BF16 TensorRT delivers a 1.7x speedup. Combined, FP8 TensorRT delivers a 2.3x performance boost on SD3.5 Large compared with running the original models in BF16 PyTorch, while using 40% less memory. And in SD3.5 Medium, BF16 TensorRT provides a 1.7x performance increase compared with BF16 PyTorch. The optimized models are now available on Stability AI’s Hugging Face page. NVIDIA and Stability AI are also collaborating to release SD3.5 as an NVIDIA NIM microservice, making it easier for creators and developers to access and deploy the model for a wide range of applications. The NIM microservice is expected to be released in July. TensorRT for RTX SDK Released Announced at Microsoft Build — and already available as part of the new Windows ML framework in preview — TensorRT for RTX is now available as a standalone SDK for developers. Previously, developers needed to pre-generate and package TensorRT engines for each class of GPU — a process that would yield GPU-specific optimizations but required significant time. With the new version of TensorRT, developers can create a generic TensorRT engine that’s optimized on device in seconds. This JIT compilation approach can be done in the background during installation or when they first use the feature. The easy-to-integrate SDK is now 8x smaller and can be invoked through Windows ML — Microsoft’s new AI inference backend in Windows. Developers can download the new standalone SDK from the NVIDIA Developer page or test it in the Windows ML preview. For more details, read this NVIDIA technical blog and this Microsoft Build recap. Join NVIDIA at GTC Paris At NVIDIA GTC Paris at VivaTech — Europe’s biggest startup and tech event — NVIDIA founder and CEO Jensen Huang yesterday delivered a keynote address on the latest breakthroughs in cloud AI infrastructure, agentic AI and physical AI. Watch a replay. GTC Paris runs through Thursday, June 12, with hands-on demos and sessions led by industry leaders. Whether attending in person or joining online, there’s still plenty to explore at the event. Each week, the RTX AI Garage blog series features community-driven AI innovations and content for those looking to learn more about NVIDIA NIM microservices and AI Blueprints, as well as building AI agents, creative workflows, digital humans, productivity apps and more on AI PCs and workstations.  Plug in to NVIDIA AI PC on Facebook, Instagram, TikTok and X — and stay informed by subscribing to the RTX AI PC newsletter. Follow NVIDIA Workstation on LinkedIn and X.  See notice regarding software product information. #nvidia #tensorrt #boosts #stable #diffusion
    BLOGS.NVIDIA.COM
    NVIDIA TensorRT Boosts Stable Diffusion 3.5 Performance on NVIDIA GeForce RTX and RTX PRO GPUs
    Generative AI has reshaped how people create, imagine and interact with digital content. As AI models continue to grow in capability and complexity, they require more VRAM, or video random access memory. The base Stable Diffusion 3.5 Large model, for example, uses over 18GB of VRAM — limiting the number of systems that can run it well. By applying quantization to the model, noncritical layers can be removed or run with lower precision. NVIDIA GeForce RTX 40 Series and the Ada Lovelace generation of NVIDIA RTX PRO GPUs support FP8 quantization to help run these quantized models, and the latest-generation NVIDIA Blackwell GPUs also add support for FP4. NVIDIA collaborated with Stability AI to quantize its latest model, Stable Diffusion (SD) 3.5 Large, to FP8 — reducing VRAM consumption by 40%. Further optimizations to SD3.5 Large and Medium with the NVIDIA TensorRT software development kit (SDK) double performance. In addition, TensorRT has been reimagined for RTX AI PCs, combining its industry-leading performance with just-in-time (JIT), on-device engine building and an 8x smaller package size for seamless AI deployment to more than 100 million RTX AI PCs. TensorRT for RTX is now available as a standalone SDK for developers. RTX-Accelerated AI NVIDIA and Stability AI are boosting the performance and reducing the VRAM requirements of Stable Diffusion 3.5, one of the world’s most popular AI image models. With NVIDIA TensorRT acceleration and quantization, users can now generate and edit images faster and more efficiently on NVIDIA RTX GPUs. Stable Diffusion 3.5 quantized FP8 (right) generates images in half the time with similar quality as FP16 (left). Prompt: A serene mountain lake at sunrise, crystal clear water reflecting snow-capped peaks, lush pine trees along the shore, soft morning mist, photorealistic, vibrant colors, high resolution. To address the VRAM limitations of SD3.5 Large, the model was quantized with TensorRT to FP8, reducing the VRAM requirement by 40% to 11GB. This means five GeForce RTX 50 Series GPUs can run the model from memory instead of just one. SD3.5 Large and Medium models were also optimized with TensorRT, an AI backend for taking full advantage of Tensor Cores. TensorRT optimizes a model’s weights and graph — the instructions on how to run a model — specifically for RTX GPUs. FP8 TensorRT boosts SD3.5 Large performance by 2.3x vs. BF16 PyTorch, with 40% less memory use. For SD3.5 Medium, BF16 TensorRT delivers a 1.7x speedup. Combined, FP8 TensorRT delivers a 2.3x performance boost on SD3.5 Large compared with running the original models in BF16 PyTorch, while using 40% less memory. And in SD3.5 Medium, BF16 TensorRT provides a 1.7x performance increase compared with BF16 PyTorch. The optimized models are now available on Stability AI’s Hugging Face page. NVIDIA and Stability AI are also collaborating to release SD3.5 as an NVIDIA NIM microservice, making it easier for creators and developers to access and deploy the model for a wide range of applications. The NIM microservice is expected to be released in July. TensorRT for RTX SDK Released Announced at Microsoft Build — and already available as part of the new Windows ML framework in preview — TensorRT for RTX is now available as a standalone SDK for developers. Previously, developers needed to pre-generate and package TensorRT engines for each class of GPU — a process that would yield GPU-specific optimizations but required significant time. With the new version of TensorRT, developers can create a generic TensorRT engine that’s optimized on device in seconds. This JIT compilation approach can be done in the background during installation or when they first use the feature. The easy-to-integrate SDK is now 8x smaller and can be invoked through Windows ML — Microsoft’s new AI inference backend in Windows. Developers can download the new standalone SDK from the NVIDIA Developer page or test it in the Windows ML preview. For more details, read this NVIDIA technical blog and this Microsoft Build recap. Join NVIDIA at GTC Paris At NVIDIA GTC Paris at VivaTech — Europe’s biggest startup and tech event — NVIDIA founder and CEO Jensen Huang yesterday delivered a keynote address on the latest breakthroughs in cloud AI infrastructure, agentic AI and physical AI. Watch a replay. GTC Paris runs through Thursday, June 12, with hands-on demos and sessions led by industry leaders. Whether attending in person or joining online, there’s still plenty to explore at the event. Each week, the RTX AI Garage blog series features community-driven AI innovations and content for those looking to learn more about NVIDIA NIM microservices and AI Blueprints, as well as building AI agents, creative workflows, digital humans, productivity apps and more on AI PCs and workstations.  Plug in to NVIDIA AI PC on Facebook, Instagram, TikTok and X — and stay informed by subscribing to the RTX AI PC newsletter. Follow NVIDIA Workstation on LinkedIn and X.  See notice regarding software product information.
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  • Looking Back at Two Classics: ILM Deploys the Fleet in ‘Star Trek: First Contact’ and ‘Rogue One: A Star Wars Story’

    Guided by visual effects supervisor John Knoll, ILM embraced continually evolving methodologies to craft breathtaking visual effects for the iconic space battles in First Contact and Rogue One.
    By Jay Stobie
    Visual effects supervisor John Knollconfers with modelmakers Kim Smith and John Goodson with the miniature of the U.S.S. Enterprise-E during production of Star Trek: First Contact.
    Bolstered by visual effects from Industrial Light & Magic, Star Trek: First Contactand Rogue One: A Star Wars Storypropelled their respective franchises to new heights. While Star Trek Generationswelcomed Captain Jean-Luc Picard’screw to the big screen, First Contact stood as the first Star Trek feature that did not focus on its original captain, the legendary James T. Kirk. Similarly, though Rogue One immediately preceded the events of Star Wars: A New Hope, it was set apart from the episodic Star Wars films and launched an era of storytelling outside of the main Skywalker saga that has gone on to include Solo: A Star Wars Story, The Mandalorian, Andor, Ahsoka, The Acolyte, and more.
    The two films also shared a key ILM contributor, John Knoll, who served as visual effects supervisor on both projects, as well as an executive producer on Rogue One. Currently, ILM’s executive creative director and senior visual effects supervisor, Knoll – who also conceived the initial framework for Rogue One’s story – guided ILM as it brought its talents to bear on these sci-fi and fantasy epics. The work involved crafting two spectacular starship-packed space clashes – First Contact’s Battle of Sector 001 and Rogue One’s Battle of Scarif. Although these iconic installments were released roughly two decades apart, they represent a captivating case study of how ILM’s approach to visual effects has evolved over time. With this in mind, let’s examine the films’ unforgettable space battles through the lens of fascinating in-universe parallels and the ILM-produced fleets that face off near Earth and Scarif.
    A final frame from the Battle of Scarif in Rogue One: A Star Wars Story.
    A Context for Conflict
    In First Contact, the United Federation of Planets – a 200-year-old interstellar government consisting of more than 150 member worlds – braces itself for an invasion by the Borg – an overwhelmingly powerful collective composed of cybernetic beings who devastate entire planets by assimilating their biological populations and technological innovations. The Borg only send a single vessel, a massive cube containing thousands of hive-minded drones and their queen, pushing the Federation’s Starfleet defenders to Earth’s doorstep. Conversely, in Rogue One, the Rebel Alliance – a fledgling coalition of freedom fighters – seeks to undermine and overthrow the stalwart Galactic Empire – a totalitarian regime preparing to tighten its grip on the galaxy by revealing a horrifying superweapon. A rebel team infiltrates a top-secret vault on Scarif in a bid to steal plans to that battle station, the dreaded Death Star, with hopes of exploiting a vulnerability in its design.
    On the surface, the situations could not seem to be more disparate, particularly in terms of the Federation’s well-established prestige and the Rebel Alliance’s haphazardly organized factions. Yet, upon closer inspection, the spaceborne conflicts at Earth and Scarif are linked by a vital commonality. The threat posed by the Borg is well-known to the Federation, but the sudden intrusion upon their space takes its defenses by surprise. Starfleet assembles any vessel within range – including antiquated Oberth-class science ships – to intercept the Borg cube in the Typhon Sector, only to be forced back to Earth on the edge of defeat. The unsanctioned mission to Scarif with Jyn Ersoand Cassian Andorand the sudden need to take down the planet’s shield gate propels the Rebel Alliance fleet into rushing to their rescue with everything from their flagship Profundity to GR-75 medium transports. Whether Federation or Rebel Alliance, these fleets gather in last-ditch efforts to oppose enemies who would embrace their eradication – the Battles of Sector 001 and Scarif are fights for survival.
    From Physical to Digital
    By the time Jonathan Frakes was selected to direct First Contact, Star Trek’s reliance on constructing traditional physical modelsfor its features was gradually giving way to innovative computer graphicsmodels, resulting in the film’s use of both techniques. “If one of the ships was to be seen full-screen and at length,” associate visual effects supervisor George Murphy told Cinefex’s Kevin H. Martin, “we knew it would be done as a stage model. Ships that would be doing a lot of elaborate maneuvers in space battle scenes would be created digitally.” In fact, physical and CG versions of the U.S.S. Enterprise-E appear in the film, with the latter being harnessed in shots involving the vessel’s entry into a temporal vortex at the conclusion of the Battle of Sector 001.
    Despite the technological leaps that ILM pioneered in the decades between First Contact and Rogue One, they considered filming physical miniatures for certain ship-related shots in the latter film. ILM considered filming physical miniatures for certain ship-related shots in Rogue One. The feature’s fleets were ultimately created digitally to allow for changes throughout post-production. “If it’s a photographed miniature element, it’s not possible to go back and make adjustments. So it’s the additional flexibility that comes with the computer graphics models that’s very attractive to many people,” John Knoll relayed to writer Jon Witmer at American Cinematographer’s TheASC.com.
    However, Knoll aimed to develop computer graphics that retained the same high-quality details as their physical counterparts, leading ILM to employ a modern approach to a time-honored modelmaking tactic. “I also wanted to emulate the kit-bashing aesthetic that had been part of Star Wars from the very beginning, where a lot of mechanical detail had been added onto the ships by using little pieces from plastic model kits,” explained Knoll in his chat with TheASC.com. For Rogue One, ILM replicated the process by obtaining such kits, scanning their parts, building a computer graphics library, and applying the CG parts to digitally modeled ships. “I’m very happy to say it was super-successful,” concluded Knoll. “I think a lot of our digital models look like they are motion-control models.”
    John Knollconfers with Kim Smith and John Goodson with the miniature of the U.S.S. Enterprise-E during production of Star Trek: First Contact.
    Legendary Lineages
    In First Contact, Captain Picard commanded a brand-new vessel, the Sovereign-class U.S.S. Enterprise-E, continuing the celebrated starship’s legacy in terms of its famous name and design aesthetic. Designed by John Eaves and developed into blueprints by Rick Sternbach, the Enterprise-E was built into a 10-foot physical model by ILM model project supervisor John Goodson and his shop’s talented team. ILM infused the ship with extraordinary detail, including viewports equipped with backlit set images from the craft’s predecessor, the U.S.S. Enterprise-D. For the vessel’s larger windows, namely those associated with the observation lounge and arboretum, ILM took a painstakingly practical approach to match the interiors shown with the real-world set pieces. “We filled that area of the model with tiny, micro-scale furniture,” Goodson informed Cinefex, “including tables and chairs.”
    Rogue One’s rebel team initially traversed the galaxy in a U-wing transport/gunship, which, much like the Enterprise-E, was a unique vessel that nonetheless channeled a certain degree of inspiration from a classic design. Lucasfilm’s Doug Chiang, a co-production designer for Rogue One, referred to the U-wing as the film’s “Huey helicopter version of an X-wing” in the Designing Rogue One bonus featurette on Disney+ before revealing that, “Towards the end of the design cycle, we actually decided that maybe we should put in more X-wing features. And so we took the X-wing engines and literally mounted them onto the configuration that we had going.” Modeled by ILM digital artist Colie Wertz, the U-wing’s final computer graphics design subtly incorporated these X-wing influences to give the transport a distinctive feel without making the craft seem out of place within the rebel fleet.
    While ILM’s work on the Enterprise-E’s viewports offered a compelling view toward the ship’s interior, a breakthrough LED setup for Rogue One permitted ILM to obtain realistic lighting on actors as they looked out from their ships and into the space around them. “All of our major spaceship cockpit scenes were done that way, with the gimbal in this giant horseshoe of LED panels we got fromVER, and we prepared graphics that went on the screens,” John Knoll shared with American Cinematographer’s Benjamin B and Jon D. Witmer. Furthermore, in Disney+’s Rogue One: Digital Storytelling bonus featurette, visual effects producer Janet Lewin noted, “For the actors, I think, in the space battle cockpits, for them to be able to see what was happening in the battle brought a higher level of accuracy to their performance.”
    The U.S.S. Enterprise-E in Star Trek: First Contact.
    Familiar Foes
    To transport First Contact’s Borg invaders, John Goodson’s team at ILM resurrected the Borg cube design previously seen in Star Trek: The Next Generationand Star Trek: Deep Space Nine, creating a nearly three-foot physical model to replace the one from the series. Art consultant and ILM veteran Bill George proposed that the cube’s seemingly straightforward layout be augmented with a complex network of photo-etched brass, a suggestion which produced a jagged surface and offered a visual that was both intricate and menacing. ILM also developed a two-foot motion-control model for a Borg sphere, a brand-new auxiliary vessel that emerged from the cube. “We vacuformed about 15 different patterns that conformed to this spherical curve and covered those with a lot of molded and cast pieces. Then we added tons of acid-etched brass over it, just like we had on the cube,” Goodson outlined to Cinefex’s Kevin H. Martin.
    As for Rogue One’s villainous fleet, reproducing the original trilogy’s Death Star and Imperial Star Destroyers centered upon translating physical models into digital assets. Although ILM no longer possessed A New Hope’s three-foot Death Star shooting model, John Knoll recreated the station’s surface paneling by gathering archival images, and as he spelled out to writer Joe Fordham in Cinefex, “I pieced all the images together. I unwrapped them into texture space and projected them onto a sphere with a trench. By doing that with enough pictures, I got pretty complete coverage of the original model, and that became a template upon which to redraw very high-resolution texture maps. Every panel, every vertical striped line, I matched from a photograph. It was as accurate as it was possible to be as a reproduction of the original model.”
    Knoll’s investigative eye continued to pay dividends when analyzing the three-foot and eight-foot Star Destroyer motion-control models, which had been built for A New Hope and Star Wars: The Empire Strikes Back, respectively. “Our general mantra was, ‘Match your memory of it more than the reality,’ because sometimes you go look at the actual prop in the archive building or you look back at the actual shot from the movie, and you go, ‘Oh, I remember it being a little better than that,’” Knoll conveyed to TheASC.com. This philosophy motivated ILM to combine elements from those two physical models into a single digital design. “Generally, we copied the three-footer for details like the superstructure on the top of the bridge, but then we copied the internal lighting plan from the eight-footer,” Knoll explained. “And then the upper surface of the three-footer was relatively undetailed because there were no shots that saw it closely, so we took a lot of the high-detail upper surface from the eight-footer. So it’s this amalgam of the two models, but the goal was to try to make it look like you remember it from A New Hope.”
    A final frame from Rogue One: A Star Wars Story.
    Forming Up the Fleets
    In addition to the U.S.S. Enterprise-E, the Battle of Sector 001 debuted numerous vessels representing four new Starfleet ship classes – the Akira, Steamrunner, Saber, and Norway – all designed by ILM visual effects art director Alex Jaeger. “Since we figured a lot of the background action in the space battle would be done with computer graphics ships that needed to be built from scratch anyway, I realized that there was no reason not to do some new designs,” John Knoll told American Cinematographer writer Ron Magid. Used in previous Star Trek projects, older physical models for the Oberth and Nebula classes were mixed into the fleet for good measure, though the vast majority of the armada originated as computer graphics.
    Over at Scarif, ILM portrayed the Rebel Alliance forces with computer graphics models of fresh designs, live-action versions of Star Wars Rebels’ VCX-100 light freighter Ghost and Hammerhead corvettes, and Star Wars staples. These ships face off against two Imperial Star Destroyers and squadrons of TIE fighters, and – upon their late arrival to the battle – Darth Vader’s Star Destroyer and the Death Star. The Tantive IV, a CR90 corvette more popularly referred to as a blockade runner, made its own special cameo at the tail end of the fight. As Princess Leia Organa’spersonal ship, the Tantive IV received the Death Star plans and fled the scene, destined to be captured by Vader’s Star Destroyer at the beginning of A New Hope. And, while we’re on the subject of intricate starship maneuvers and space-based choreography…
    Although the First Contact team could plan visual effects shots with animated storyboards, ILM supplied Gareth Edwards with a next-level virtual viewfinder that allowed the director to select his shots by immersing himself among Rogue One’s ships in real time. “What we wanted to do is give Gareth the opportunity to shoot his space battles and other all-digital scenes the same way he shoots his live-action. Then he could go in with this sort of virtual viewfinder and view the space battle going on, and figure out what the best angle was to shoot those ships from,” senior animation supervisor Hal Hickel described in the Rogue One: Digital Storytelling featurette. Hickel divulged that the sequence involving the dish array docking with the Death Star was an example of the “spontaneous discovery of great angles,” as the scene was never storyboarded or previsualized.
    Visual effects supervisor John Knoll with director Gareth Edwards during production of Rogue One: A Star Wars Story.
    Tough Little Ships
    The Federation and Rebel Alliance each deployed “tough little ships”in their respective conflicts, namely the U.S.S. Defiant from Deep Space Nine and the Tantive IV from A New Hope. VisionArt had already built a CG Defiant for the Deep Space Nine series, but ILM upgraded the model with images gathered from the ship’s three-foot physical model. A similar tactic was taken to bring the Tantive IV into the digital realm for Rogue One. “This was the Blockade Runner. This was the most accurate 1:1 reproduction we could possibly have made,” model supervisor Russell Paul declared to Cinefex’s Joe Fordham. “We did an extensive photo reference shoot and photogrammetry re-creation of the miniature. From there, we built it out as accurately as possible.” Speaking of sturdy ships, if you look very closely, you can spot a model of the Millennium Falcon flashing across the background as the U.S.S. Defiant makes an attack run on the Borg cube at the Battle of Sector 001!
    Exploration and Hope
    The in-universe ramifications that materialize from the Battles of Sector 001 and Scarif are monumental. The destruction of the Borg cube compels the Borg Queen to travel back in time in an attempt to vanquish Earth before the Federation can even be formed, but Captain Picard and the Enterprise-E foil the plot and end up helping their 21st century ancestors make “first contact” with another species, the logic-revering Vulcans. The post-Scarif benefits take longer to play out for the Rebel Alliance, but the theft of the Death Star plans eventually leads to the superweapon’s destruction. The Galactic Civil War is far from over, but Scarif is a significant step in the Alliance’s effort to overthrow the Empire.
    The visual effects ILM provided for First Contact and Rogue One contributed significantly to the critical and commercial acclaim both pictures enjoyed, a victory reflecting the relentless dedication, tireless work ethic, and innovative spirit embodied by visual effects supervisor John Knoll and ILM’s entire staff. While being interviewed for The Making of Star Trek: First Contact, actor Patrick Stewart praised ILM’s invaluable influence, emphasizing, “ILM was with us, on this movie, almost every day on set. There is so much that they are involved in.” And, regardless of your personal preferences – phasers or lasers, photon torpedoes or proton torpedoes, warp speed or hyperspace – perhaps Industrial Light & Magic’s ability to infuse excitement into both franchises demonstrates that Star Trek and Star Wars encompass themes that are not competitive, but compatible. After all, what goes together better than exploration and hope?

    Jay Stobieis a writer, author, and consultant who has contributed articles to ILM.com, Skysound.com, Star Wars Insider, StarWars.com, Star Trek Explorer, Star Trek Magazine, and StarTrek.com. Jay loves sci-fi, fantasy, and film, and you can learn more about him by visiting JayStobie.com or finding him on Twitter, Instagram, and other social media platforms at @StobiesGalaxy.
    #looking #back #two #classics #ilm
    Looking Back at Two Classics: ILM Deploys the Fleet in ‘Star Trek: First Contact’ and ‘Rogue One: A Star Wars Story’
    Guided by visual effects supervisor John Knoll, ILM embraced continually evolving methodologies to craft breathtaking visual effects for the iconic space battles in First Contact and Rogue One. By Jay Stobie Visual effects supervisor John Knollconfers with modelmakers Kim Smith and John Goodson with the miniature of the U.S.S. Enterprise-E during production of Star Trek: First Contact. Bolstered by visual effects from Industrial Light & Magic, Star Trek: First Contactand Rogue One: A Star Wars Storypropelled their respective franchises to new heights. While Star Trek Generationswelcomed Captain Jean-Luc Picard’screw to the big screen, First Contact stood as the first Star Trek feature that did not focus on its original captain, the legendary James T. Kirk. Similarly, though Rogue One immediately preceded the events of Star Wars: A New Hope, it was set apart from the episodic Star Wars films and launched an era of storytelling outside of the main Skywalker saga that has gone on to include Solo: A Star Wars Story, The Mandalorian, Andor, Ahsoka, The Acolyte, and more. The two films also shared a key ILM contributor, John Knoll, who served as visual effects supervisor on both projects, as well as an executive producer on Rogue One. Currently, ILM’s executive creative director and senior visual effects supervisor, Knoll – who also conceived the initial framework for Rogue One’s story – guided ILM as it brought its talents to bear on these sci-fi and fantasy epics. The work involved crafting two spectacular starship-packed space clashes – First Contact’s Battle of Sector 001 and Rogue One’s Battle of Scarif. Although these iconic installments were released roughly two decades apart, they represent a captivating case study of how ILM’s approach to visual effects has evolved over time. With this in mind, let’s examine the films’ unforgettable space battles through the lens of fascinating in-universe parallels and the ILM-produced fleets that face off near Earth and Scarif. A final frame from the Battle of Scarif in Rogue One: A Star Wars Story. A Context for Conflict In First Contact, the United Federation of Planets – a 200-year-old interstellar government consisting of more than 150 member worlds – braces itself for an invasion by the Borg – an overwhelmingly powerful collective composed of cybernetic beings who devastate entire planets by assimilating their biological populations and technological innovations. The Borg only send a single vessel, a massive cube containing thousands of hive-minded drones and their queen, pushing the Federation’s Starfleet defenders to Earth’s doorstep. Conversely, in Rogue One, the Rebel Alliance – a fledgling coalition of freedom fighters – seeks to undermine and overthrow the stalwart Galactic Empire – a totalitarian regime preparing to tighten its grip on the galaxy by revealing a horrifying superweapon. A rebel team infiltrates a top-secret vault on Scarif in a bid to steal plans to that battle station, the dreaded Death Star, with hopes of exploiting a vulnerability in its design. On the surface, the situations could not seem to be more disparate, particularly in terms of the Federation’s well-established prestige and the Rebel Alliance’s haphazardly organized factions. Yet, upon closer inspection, the spaceborne conflicts at Earth and Scarif are linked by a vital commonality. The threat posed by the Borg is well-known to the Federation, but the sudden intrusion upon their space takes its defenses by surprise. Starfleet assembles any vessel within range – including antiquated Oberth-class science ships – to intercept the Borg cube in the Typhon Sector, only to be forced back to Earth on the edge of defeat. The unsanctioned mission to Scarif with Jyn Ersoand Cassian Andorand the sudden need to take down the planet’s shield gate propels the Rebel Alliance fleet into rushing to their rescue with everything from their flagship Profundity to GR-75 medium transports. Whether Federation or Rebel Alliance, these fleets gather in last-ditch efforts to oppose enemies who would embrace their eradication – the Battles of Sector 001 and Scarif are fights for survival. From Physical to Digital By the time Jonathan Frakes was selected to direct First Contact, Star Trek’s reliance on constructing traditional physical modelsfor its features was gradually giving way to innovative computer graphicsmodels, resulting in the film’s use of both techniques. “If one of the ships was to be seen full-screen and at length,” associate visual effects supervisor George Murphy told Cinefex’s Kevin H. Martin, “we knew it would be done as a stage model. Ships that would be doing a lot of elaborate maneuvers in space battle scenes would be created digitally.” In fact, physical and CG versions of the U.S.S. Enterprise-E appear in the film, with the latter being harnessed in shots involving the vessel’s entry into a temporal vortex at the conclusion of the Battle of Sector 001. Despite the technological leaps that ILM pioneered in the decades between First Contact and Rogue One, they considered filming physical miniatures for certain ship-related shots in the latter film. ILM considered filming physical miniatures for certain ship-related shots in Rogue One. The feature’s fleets were ultimately created digitally to allow for changes throughout post-production. “If it’s a photographed miniature element, it’s not possible to go back and make adjustments. So it’s the additional flexibility that comes with the computer graphics models that’s very attractive to many people,” John Knoll relayed to writer Jon Witmer at American Cinematographer’s TheASC.com. However, Knoll aimed to develop computer graphics that retained the same high-quality details as their physical counterparts, leading ILM to employ a modern approach to a time-honored modelmaking tactic. “I also wanted to emulate the kit-bashing aesthetic that had been part of Star Wars from the very beginning, where a lot of mechanical detail had been added onto the ships by using little pieces from plastic model kits,” explained Knoll in his chat with TheASC.com. For Rogue One, ILM replicated the process by obtaining such kits, scanning their parts, building a computer graphics library, and applying the CG parts to digitally modeled ships. “I’m very happy to say it was super-successful,” concluded Knoll. “I think a lot of our digital models look like they are motion-control models.” John Knollconfers with Kim Smith and John Goodson with the miniature of the U.S.S. Enterprise-E during production of Star Trek: First Contact. Legendary Lineages In First Contact, Captain Picard commanded a brand-new vessel, the Sovereign-class U.S.S. Enterprise-E, continuing the celebrated starship’s legacy in terms of its famous name and design aesthetic. Designed by John Eaves and developed into blueprints by Rick Sternbach, the Enterprise-E was built into a 10-foot physical model by ILM model project supervisor John Goodson and his shop’s talented team. ILM infused the ship with extraordinary detail, including viewports equipped with backlit set images from the craft’s predecessor, the U.S.S. Enterprise-D. For the vessel’s larger windows, namely those associated with the observation lounge and arboretum, ILM took a painstakingly practical approach to match the interiors shown with the real-world set pieces. “We filled that area of the model with tiny, micro-scale furniture,” Goodson informed Cinefex, “including tables and chairs.” Rogue One’s rebel team initially traversed the galaxy in a U-wing transport/gunship, which, much like the Enterprise-E, was a unique vessel that nonetheless channeled a certain degree of inspiration from a classic design. Lucasfilm’s Doug Chiang, a co-production designer for Rogue One, referred to the U-wing as the film’s “Huey helicopter version of an X-wing” in the Designing Rogue One bonus featurette on Disney+ before revealing that, “Towards the end of the design cycle, we actually decided that maybe we should put in more X-wing features. And so we took the X-wing engines and literally mounted them onto the configuration that we had going.” Modeled by ILM digital artist Colie Wertz, the U-wing’s final computer graphics design subtly incorporated these X-wing influences to give the transport a distinctive feel without making the craft seem out of place within the rebel fleet. While ILM’s work on the Enterprise-E’s viewports offered a compelling view toward the ship’s interior, a breakthrough LED setup for Rogue One permitted ILM to obtain realistic lighting on actors as they looked out from their ships and into the space around them. “All of our major spaceship cockpit scenes were done that way, with the gimbal in this giant horseshoe of LED panels we got fromVER, and we prepared graphics that went on the screens,” John Knoll shared with American Cinematographer’s Benjamin B and Jon D. Witmer. Furthermore, in Disney+’s Rogue One: Digital Storytelling bonus featurette, visual effects producer Janet Lewin noted, “For the actors, I think, in the space battle cockpits, for them to be able to see what was happening in the battle brought a higher level of accuracy to their performance.” The U.S.S. Enterprise-E in Star Trek: First Contact. Familiar Foes To transport First Contact’s Borg invaders, John Goodson’s team at ILM resurrected the Borg cube design previously seen in Star Trek: The Next Generationand Star Trek: Deep Space Nine, creating a nearly three-foot physical model to replace the one from the series. Art consultant and ILM veteran Bill George proposed that the cube’s seemingly straightforward layout be augmented with a complex network of photo-etched brass, a suggestion which produced a jagged surface and offered a visual that was both intricate and menacing. ILM also developed a two-foot motion-control model for a Borg sphere, a brand-new auxiliary vessel that emerged from the cube. “We vacuformed about 15 different patterns that conformed to this spherical curve and covered those with a lot of molded and cast pieces. Then we added tons of acid-etched brass over it, just like we had on the cube,” Goodson outlined to Cinefex’s Kevin H. Martin. As for Rogue One’s villainous fleet, reproducing the original trilogy’s Death Star and Imperial Star Destroyers centered upon translating physical models into digital assets. Although ILM no longer possessed A New Hope’s three-foot Death Star shooting model, John Knoll recreated the station’s surface paneling by gathering archival images, and as he spelled out to writer Joe Fordham in Cinefex, “I pieced all the images together. I unwrapped them into texture space and projected them onto a sphere with a trench. By doing that with enough pictures, I got pretty complete coverage of the original model, and that became a template upon which to redraw very high-resolution texture maps. Every panel, every vertical striped line, I matched from a photograph. It was as accurate as it was possible to be as a reproduction of the original model.” Knoll’s investigative eye continued to pay dividends when analyzing the three-foot and eight-foot Star Destroyer motion-control models, which had been built for A New Hope and Star Wars: The Empire Strikes Back, respectively. “Our general mantra was, ‘Match your memory of it more than the reality,’ because sometimes you go look at the actual prop in the archive building or you look back at the actual shot from the movie, and you go, ‘Oh, I remember it being a little better than that,’” Knoll conveyed to TheASC.com. This philosophy motivated ILM to combine elements from those two physical models into a single digital design. “Generally, we copied the three-footer for details like the superstructure on the top of the bridge, but then we copied the internal lighting plan from the eight-footer,” Knoll explained. “And then the upper surface of the three-footer was relatively undetailed because there were no shots that saw it closely, so we took a lot of the high-detail upper surface from the eight-footer. So it’s this amalgam of the two models, but the goal was to try to make it look like you remember it from A New Hope.” A final frame from Rogue One: A Star Wars Story. Forming Up the Fleets In addition to the U.S.S. Enterprise-E, the Battle of Sector 001 debuted numerous vessels representing four new Starfleet ship classes – the Akira, Steamrunner, Saber, and Norway – all designed by ILM visual effects art director Alex Jaeger. “Since we figured a lot of the background action in the space battle would be done with computer graphics ships that needed to be built from scratch anyway, I realized that there was no reason not to do some new designs,” John Knoll told American Cinematographer writer Ron Magid. Used in previous Star Trek projects, older physical models for the Oberth and Nebula classes were mixed into the fleet for good measure, though the vast majority of the armada originated as computer graphics. Over at Scarif, ILM portrayed the Rebel Alliance forces with computer graphics models of fresh designs, live-action versions of Star Wars Rebels’ VCX-100 light freighter Ghost and Hammerhead corvettes, and Star Wars staples. These ships face off against two Imperial Star Destroyers and squadrons of TIE fighters, and – upon their late arrival to the battle – Darth Vader’s Star Destroyer and the Death Star. The Tantive IV, a CR90 corvette more popularly referred to as a blockade runner, made its own special cameo at the tail end of the fight. As Princess Leia Organa’spersonal ship, the Tantive IV received the Death Star plans and fled the scene, destined to be captured by Vader’s Star Destroyer at the beginning of A New Hope. And, while we’re on the subject of intricate starship maneuvers and space-based choreography… Although the First Contact team could plan visual effects shots with animated storyboards, ILM supplied Gareth Edwards with a next-level virtual viewfinder that allowed the director to select his shots by immersing himself among Rogue One’s ships in real time. “What we wanted to do is give Gareth the opportunity to shoot his space battles and other all-digital scenes the same way he shoots his live-action. Then he could go in with this sort of virtual viewfinder and view the space battle going on, and figure out what the best angle was to shoot those ships from,” senior animation supervisor Hal Hickel described in the Rogue One: Digital Storytelling featurette. Hickel divulged that the sequence involving the dish array docking with the Death Star was an example of the “spontaneous discovery of great angles,” as the scene was never storyboarded or previsualized. Visual effects supervisor John Knoll with director Gareth Edwards during production of Rogue One: A Star Wars Story. Tough Little Ships The Federation and Rebel Alliance each deployed “tough little ships”in their respective conflicts, namely the U.S.S. Defiant from Deep Space Nine and the Tantive IV from A New Hope. VisionArt had already built a CG Defiant for the Deep Space Nine series, but ILM upgraded the model with images gathered from the ship’s three-foot physical model. A similar tactic was taken to bring the Tantive IV into the digital realm for Rogue One. “This was the Blockade Runner. This was the most accurate 1:1 reproduction we could possibly have made,” model supervisor Russell Paul declared to Cinefex’s Joe Fordham. “We did an extensive photo reference shoot and photogrammetry re-creation of the miniature. From there, we built it out as accurately as possible.” Speaking of sturdy ships, if you look very closely, you can spot a model of the Millennium Falcon flashing across the background as the U.S.S. Defiant makes an attack run on the Borg cube at the Battle of Sector 001! Exploration and Hope The in-universe ramifications that materialize from the Battles of Sector 001 and Scarif are monumental. The destruction of the Borg cube compels the Borg Queen to travel back in time in an attempt to vanquish Earth before the Federation can even be formed, but Captain Picard and the Enterprise-E foil the plot and end up helping their 21st century ancestors make “first contact” with another species, the logic-revering Vulcans. The post-Scarif benefits take longer to play out for the Rebel Alliance, but the theft of the Death Star plans eventually leads to the superweapon’s destruction. The Galactic Civil War is far from over, but Scarif is a significant step in the Alliance’s effort to overthrow the Empire. The visual effects ILM provided for First Contact and Rogue One contributed significantly to the critical and commercial acclaim both pictures enjoyed, a victory reflecting the relentless dedication, tireless work ethic, and innovative spirit embodied by visual effects supervisor John Knoll and ILM’s entire staff. While being interviewed for The Making of Star Trek: First Contact, actor Patrick Stewart praised ILM’s invaluable influence, emphasizing, “ILM was with us, on this movie, almost every day on set. There is so much that they are involved in.” And, regardless of your personal preferences – phasers or lasers, photon torpedoes or proton torpedoes, warp speed or hyperspace – perhaps Industrial Light & Magic’s ability to infuse excitement into both franchises demonstrates that Star Trek and Star Wars encompass themes that are not competitive, but compatible. After all, what goes together better than exploration and hope? – Jay Stobieis a writer, author, and consultant who has contributed articles to ILM.com, Skysound.com, Star Wars Insider, StarWars.com, Star Trek Explorer, Star Trek Magazine, and StarTrek.com. Jay loves sci-fi, fantasy, and film, and you can learn more about him by visiting JayStobie.com or finding him on Twitter, Instagram, and other social media platforms at @StobiesGalaxy. #looking #back #two #classics #ilm
    WWW.ILM.COM
    Looking Back at Two Classics: ILM Deploys the Fleet in ‘Star Trek: First Contact’ and ‘Rogue One: A Star Wars Story’
    Guided by visual effects supervisor John Knoll, ILM embraced continually evolving methodologies to craft breathtaking visual effects for the iconic space battles in First Contact and Rogue One. By Jay Stobie Visual effects supervisor John Knoll (right) confers with modelmakers Kim Smith and John Goodson with the miniature of the U.S.S. Enterprise-E during production of Star Trek: First Contact (Credit: ILM). Bolstered by visual effects from Industrial Light & Magic, Star Trek: First Contact (1996) and Rogue One: A Star Wars Story (2016) propelled their respective franchises to new heights. While Star Trek Generations (1994) welcomed Captain Jean-Luc Picard’s (Patrick Stewart) crew to the big screen, First Contact stood as the first Star Trek feature that did not focus on its original captain, the legendary James T. Kirk (William Shatner). Similarly, though Rogue One immediately preceded the events of Star Wars: A New Hope (1977), it was set apart from the episodic Star Wars films and launched an era of storytelling outside of the main Skywalker saga that has gone on to include Solo: A Star Wars Story (2018), The Mandalorian (2019-23), Andor (2022-25), Ahsoka (2023), The Acolyte (2024), and more. The two films also shared a key ILM contributor, John Knoll, who served as visual effects supervisor on both projects, as well as an executive producer on Rogue One. Currently, ILM’s executive creative director and senior visual effects supervisor, Knoll – who also conceived the initial framework for Rogue One’s story – guided ILM as it brought its talents to bear on these sci-fi and fantasy epics. The work involved crafting two spectacular starship-packed space clashes – First Contact’s Battle of Sector 001 and Rogue One’s Battle of Scarif. Although these iconic installments were released roughly two decades apart, they represent a captivating case study of how ILM’s approach to visual effects has evolved over time. With this in mind, let’s examine the films’ unforgettable space battles through the lens of fascinating in-universe parallels and the ILM-produced fleets that face off near Earth and Scarif. A final frame from the Battle of Scarif in Rogue One: A Star Wars Story (Credit: ILM & Lucasfilm). A Context for Conflict In First Contact, the United Federation of Planets – a 200-year-old interstellar government consisting of more than 150 member worlds – braces itself for an invasion by the Borg – an overwhelmingly powerful collective composed of cybernetic beings who devastate entire planets by assimilating their biological populations and technological innovations. The Borg only send a single vessel, a massive cube containing thousands of hive-minded drones and their queen, pushing the Federation’s Starfleet defenders to Earth’s doorstep. Conversely, in Rogue One, the Rebel Alliance – a fledgling coalition of freedom fighters – seeks to undermine and overthrow the stalwart Galactic Empire – a totalitarian regime preparing to tighten its grip on the galaxy by revealing a horrifying superweapon. A rebel team infiltrates a top-secret vault on Scarif in a bid to steal plans to that battle station, the dreaded Death Star, with hopes of exploiting a vulnerability in its design. On the surface, the situations could not seem to be more disparate, particularly in terms of the Federation’s well-established prestige and the Rebel Alliance’s haphazardly organized factions. Yet, upon closer inspection, the spaceborne conflicts at Earth and Scarif are linked by a vital commonality. The threat posed by the Borg is well-known to the Federation, but the sudden intrusion upon their space takes its defenses by surprise. Starfleet assembles any vessel within range – including antiquated Oberth-class science ships – to intercept the Borg cube in the Typhon Sector, only to be forced back to Earth on the edge of defeat. The unsanctioned mission to Scarif with Jyn Erso (Felicity Jones) and Cassian Andor (Diego Luna) and the sudden need to take down the planet’s shield gate propels the Rebel Alliance fleet into rushing to their rescue with everything from their flagship Profundity to GR-75 medium transports. Whether Federation or Rebel Alliance, these fleets gather in last-ditch efforts to oppose enemies who would embrace their eradication – the Battles of Sector 001 and Scarif are fights for survival. From Physical to Digital By the time Jonathan Frakes was selected to direct First Contact, Star Trek’s reliance on constructing traditional physical models (many of which were built by ILM) for its features was gradually giving way to innovative computer graphics (CG) models, resulting in the film’s use of both techniques. “If one of the ships was to be seen full-screen and at length,” associate visual effects supervisor George Murphy told Cinefex’s Kevin H. Martin, “we knew it would be done as a stage model. Ships that would be doing a lot of elaborate maneuvers in space battle scenes would be created digitally.” In fact, physical and CG versions of the U.S.S. Enterprise-E appear in the film, with the latter being harnessed in shots involving the vessel’s entry into a temporal vortex at the conclusion of the Battle of Sector 001. Despite the technological leaps that ILM pioneered in the decades between First Contact and Rogue One, they considered filming physical miniatures for certain ship-related shots in the latter film. ILM considered filming physical miniatures for certain ship-related shots in Rogue One. The feature’s fleets were ultimately created digitally to allow for changes throughout post-production. “If it’s a photographed miniature element, it’s not possible to go back and make adjustments. So it’s the additional flexibility that comes with the computer graphics models that’s very attractive to many people,” John Knoll relayed to writer Jon Witmer at American Cinematographer’s TheASC.com. However, Knoll aimed to develop computer graphics that retained the same high-quality details as their physical counterparts, leading ILM to employ a modern approach to a time-honored modelmaking tactic. “I also wanted to emulate the kit-bashing aesthetic that had been part of Star Wars from the very beginning, where a lot of mechanical detail had been added onto the ships by using little pieces from plastic model kits,” explained Knoll in his chat with TheASC.com. For Rogue One, ILM replicated the process by obtaining such kits, scanning their parts, building a computer graphics library, and applying the CG parts to digitally modeled ships. “I’m very happy to say it was super-successful,” concluded Knoll. “I think a lot of our digital models look like they are motion-control models.” John Knoll (second from left) confers with Kim Smith and John Goodson with the miniature of the U.S.S. Enterprise-E during production of Star Trek: First Contact (Credit: ILM). Legendary Lineages In First Contact, Captain Picard commanded a brand-new vessel, the Sovereign-class U.S.S. Enterprise-E, continuing the celebrated starship’s legacy in terms of its famous name and design aesthetic. Designed by John Eaves and developed into blueprints by Rick Sternbach, the Enterprise-E was built into a 10-foot physical model by ILM model project supervisor John Goodson and his shop’s talented team. ILM infused the ship with extraordinary detail, including viewports equipped with backlit set images from the craft’s predecessor, the U.S.S. Enterprise-D. For the vessel’s larger windows, namely those associated with the observation lounge and arboretum, ILM took a painstakingly practical approach to match the interiors shown with the real-world set pieces. “We filled that area of the model with tiny, micro-scale furniture,” Goodson informed Cinefex, “including tables and chairs.” Rogue One’s rebel team initially traversed the galaxy in a U-wing transport/gunship, which, much like the Enterprise-E, was a unique vessel that nonetheless channeled a certain degree of inspiration from a classic design. Lucasfilm’s Doug Chiang, a co-production designer for Rogue One, referred to the U-wing as the film’s “Huey helicopter version of an X-wing” in the Designing Rogue One bonus featurette on Disney+ before revealing that, “Towards the end of the design cycle, we actually decided that maybe we should put in more X-wing features. And so we took the X-wing engines and literally mounted them onto the configuration that we had going.” Modeled by ILM digital artist Colie Wertz, the U-wing’s final computer graphics design subtly incorporated these X-wing influences to give the transport a distinctive feel without making the craft seem out of place within the rebel fleet. While ILM’s work on the Enterprise-E’s viewports offered a compelling view toward the ship’s interior, a breakthrough LED setup for Rogue One permitted ILM to obtain realistic lighting on actors as they looked out from their ships and into the space around them. “All of our major spaceship cockpit scenes were done that way, with the gimbal in this giant horseshoe of LED panels we got from [equipment vendor] VER, and we prepared graphics that went on the screens,” John Knoll shared with American Cinematographer’s Benjamin B and Jon D. Witmer. Furthermore, in Disney+’s Rogue One: Digital Storytelling bonus featurette, visual effects producer Janet Lewin noted, “For the actors, I think, in the space battle cockpits, for them to be able to see what was happening in the battle brought a higher level of accuracy to their performance.” The U.S.S. Enterprise-E in Star Trek: First Contact (Credit: Paramount). Familiar Foes To transport First Contact’s Borg invaders, John Goodson’s team at ILM resurrected the Borg cube design previously seen in Star Trek: The Next Generation (1987) and Star Trek: Deep Space Nine (1993), creating a nearly three-foot physical model to replace the one from the series. Art consultant and ILM veteran Bill George proposed that the cube’s seemingly straightforward layout be augmented with a complex network of photo-etched brass, a suggestion which produced a jagged surface and offered a visual that was both intricate and menacing. ILM also developed a two-foot motion-control model for a Borg sphere, a brand-new auxiliary vessel that emerged from the cube. “We vacuformed about 15 different patterns that conformed to this spherical curve and covered those with a lot of molded and cast pieces. Then we added tons of acid-etched brass over it, just like we had on the cube,” Goodson outlined to Cinefex’s Kevin H. Martin. As for Rogue One’s villainous fleet, reproducing the original trilogy’s Death Star and Imperial Star Destroyers centered upon translating physical models into digital assets. Although ILM no longer possessed A New Hope’s three-foot Death Star shooting model, John Knoll recreated the station’s surface paneling by gathering archival images, and as he spelled out to writer Joe Fordham in Cinefex, “I pieced all the images together. I unwrapped them into texture space and projected them onto a sphere with a trench. By doing that with enough pictures, I got pretty complete coverage of the original model, and that became a template upon which to redraw very high-resolution texture maps. Every panel, every vertical striped line, I matched from a photograph. It was as accurate as it was possible to be as a reproduction of the original model.” Knoll’s investigative eye continued to pay dividends when analyzing the three-foot and eight-foot Star Destroyer motion-control models, which had been built for A New Hope and Star Wars: The Empire Strikes Back (1980), respectively. “Our general mantra was, ‘Match your memory of it more than the reality,’ because sometimes you go look at the actual prop in the archive building or you look back at the actual shot from the movie, and you go, ‘Oh, I remember it being a little better than that,’” Knoll conveyed to TheASC.com. This philosophy motivated ILM to combine elements from those two physical models into a single digital design. “Generally, we copied the three-footer for details like the superstructure on the top of the bridge, but then we copied the internal lighting plan from the eight-footer,” Knoll explained. “And then the upper surface of the three-footer was relatively undetailed because there were no shots that saw it closely, so we took a lot of the high-detail upper surface from the eight-footer. So it’s this amalgam of the two models, but the goal was to try to make it look like you remember it from A New Hope.” A final frame from Rogue One: A Star Wars Story (Credit: ILM & Lucasfilm). Forming Up the Fleets In addition to the U.S.S. Enterprise-E, the Battle of Sector 001 debuted numerous vessels representing four new Starfleet ship classes – the Akira, Steamrunner, Saber, and Norway – all designed by ILM visual effects art director Alex Jaeger. “Since we figured a lot of the background action in the space battle would be done with computer graphics ships that needed to be built from scratch anyway, I realized that there was no reason not to do some new designs,” John Knoll told American Cinematographer writer Ron Magid. Used in previous Star Trek projects, older physical models for the Oberth and Nebula classes were mixed into the fleet for good measure, though the vast majority of the armada originated as computer graphics. Over at Scarif, ILM portrayed the Rebel Alliance forces with computer graphics models of fresh designs (the MC75 cruiser Profundity and U-wings), live-action versions of Star Wars Rebels’ VCX-100 light freighter Ghost and Hammerhead corvettes, and Star Wars staples (Nebulon-B frigates, X-wings, Y-wings, and more). These ships face off against two Imperial Star Destroyers and squadrons of TIE fighters, and – upon their late arrival to the battle – Darth Vader’s Star Destroyer and the Death Star. The Tantive IV, a CR90 corvette more popularly referred to as a blockade runner, made its own special cameo at the tail end of the fight. As Princess Leia Organa’s (Carrie Fisher and Ingvild Deila) personal ship, the Tantive IV received the Death Star plans and fled the scene, destined to be captured by Vader’s Star Destroyer at the beginning of A New Hope. And, while we’re on the subject of intricate starship maneuvers and space-based choreography… Although the First Contact team could plan visual effects shots with animated storyboards, ILM supplied Gareth Edwards with a next-level virtual viewfinder that allowed the director to select his shots by immersing himself among Rogue One’s ships in real time. “What we wanted to do is give Gareth the opportunity to shoot his space battles and other all-digital scenes the same way he shoots his live-action. Then he could go in with this sort of virtual viewfinder and view the space battle going on, and figure out what the best angle was to shoot those ships from,” senior animation supervisor Hal Hickel described in the Rogue One: Digital Storytelling featurette. Hickel divulged that the sequence involving the dish array docking with the Death Star was an example of the “spontaneous discovery of great angles,” as the scene was never storyboarded or previsualized. Visual effects supervisor John Knoll with director Gareth Edwards during production of Rogue One: A Star Wars Story (Credit: ILM & Lucasfilm). Tough Little Ships The Federation and Rebel Alliance each deployed “tough little ships” (an endearing description Commander William T. Riker [Jonathan Frakes] bestowed upon the U.S.S. Defiant in First Contact) in their respective conflicts, namely the U.S.S. Defiant from Deep Space Nine and the Tantive IV from A New Hope. VisionArt had already built a CG Defiant for the Deep Space Nine series, but ILM upgraded the model with images gathered from the ship’s three-foot physical model. A similar tactic was taken to bring the Tantive IV into the digital realm for Rogue One. “This was the Blockade Runner. This was the most accurate 1:1 reproduction we could possibly have made,” model supervisor Russell Paul declared to Cinefex’s Joe Fordham. “We did an extensive photo reference shoot and photogrammetry re-creation of the miniature. From there, we built it out as accurately as possible.” Speaking of sturdy ships, if you look very closely, you can spot a model of the Millennium Falcon flashing across the background as the U.S.S. Defiant makes an attack run on the Borg cube at the Battle of Sector 001! Exploration and Hope The in-universe ramifications that materialize from the Battles of Sector 001 and Scarif are monumental. The destruction of the Borg cube compels the Borg Queen to travel back in time in an attempt to vanquish Earth before the Federation can even be formed, but Captain Picard and the Enterprise-E foil the plot and end up helping their 21st century ancestors make “first contact” with another species, the logic-revering Vulcans. The post-Scarif benefits take longer to play out for the Rebel Alliance, but the theft of the Death Star plans eventually leads to the superweapon’s destruction. The Galactic Civil War is far from over, but Scarif is a significant step in the Alliance’s effort to overthrow the Empire. The visual effects ILM provided for First Contact and Rogue One contributed significantly to the critical and commercial acclaim both pictures enjoyed, a victory reflecting the relentless dedication, tireless work ethic, and innovative spirit embodied by visual effects supervisor John Knoll and ILM’s entire staff. While being interviewed for The Making of Star Trek: First Contact, actor Patrick Stewart praised ILM’s invaluable influence, emphasizing, “ILM was with us, on this movie, almost every day on set. There is so much that they are involved in.” And, regardless of your personal preferences – phasers or lasers, photon torpedoes or proton torpedoes, warp speed or hyperspace – perhaps Industrial Light & Magic’s ability to infuse excitement into both franchises demonstrates that Star Trek and Star Wars encompass themes that are not competitive, but compatible. After all, what goes together better than exploration and hope? – Jay Stobie (he/him) is a writer, author, and consultant who has contributed articles to ILM.com, Skysound.com, Star Wars Insider, StarWars.com, Star Trek Explorer, Star Trek Magazine, and StarTrek.com. Jay loves sci-fi, fantasy, and film, and you can learn more about him by visiting JayStobie.com or finding him on Twitter, Instagram, and other social media platforms at @StobiesGalaxy.
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  • Paper Architecture: From Soviet Subversion to Zaha’s Suprematism

    Architizer’s Vision Awards are back! The global awards program honors the world’s best architectural concepts, ideas and imagery. Submit your work ahead of the Final Entry Deadline on July 11th!
    Behind the term “paper architecture” hides a strange paradox: the radical act of building without, well, building. Paper architecture is usually associated with speculative design projects, presented in the form of drawings, which can also be considered art pieces. However, even though it is often dismissed as a mere utopian or academic exercise, paper architecture has historically served as a powerful form of protest, advocating against political regimes, architectural orthodoxy or cultural stagnation.
    Unbound by real-world limitations such as materials, regulations and budgets, paper architects are free to focus on the messages behind their designs rather than constantly striving for their implementation. In parallel, due to its subtleness, paper architecture has become a platform that enables radical commentary via a rather “safe” medium. Instead of relying on more traditional forms of protestthis powerful visual language, combined with scrupulous aesthetics and imagination can start a more formidable “behind-the-scenes rebellion”.
    Unearthing Nostalgia by Bruno Xavier & Michelle Ashley Ovanessians, A+ Vision Awards, 2023
    Perhaps the most well-known paper architects, Archigram was a radical British collective that was formed in the 1960s in London. Their work Walking City or Plug-In City showcased visions of a playful, technologically driven architecture that deeply contrasted and, by extent, protested against the rigid regime of post-war modernism and its extensive bureaucracy. This pop-art-style architecture served as a powerful critique towards the saturated idea of functional monotony.
    Additionally, the Russian architect, artist, and curator, Yuri Avvakumuv introduced the term “paper architecture” within the restrictive cultural and political climate of late Soviet Russia. Having to deal with heavy censorship, Avvakumuv turned to competitions and speculative drawings in an attempt resist that dominance of totalitarian architecture. Poetic, deeply allegorical and oftentimes ironic architectural renderings, critiqued the bureaucratic sterility of Soviet planning and the state-mandated architectural principles architects had to follow. Consequently, this profound demonstration of un-built architecture within the specific setting, turned into a collective cultural wave that advocated artistic autonomy and expression for the built environment.
    Klothos’ Loom of Memories by Ioana Alexandra Enache, A+ Vision Awards, 2023
    The Amerian architect Lebbeus Woods was also one of the most intellectually intense practitioners of paper architecture, whose work touches upon global issues on war zones and urban trauma. His imaginative, post-apocalyptic cities opened up discussions for rebuilding after destruction. Works such as War and Architecture and Underground Berlin, albeit “dystopic”, acted as moral propositions, exploring potential reconstructions that would “heal” these cities. Through his drawings, he rigorously investigated and examined scenarios of ethical rebuilding, refusing to comply to the principles of popular commerce, and instead creating a new architectural practice of political resistance.
    Finally, operating within a very male-dominated world, Zaha Hadid’s earlier work — particularly on Malevich — served as a protesting tool on multiple levels. Influenced by Suprematist aesthetics, her bold, dynamic compositions stood against the formal conservatism of architectural ideas, where the design must always yield to gravity and function. In parallel, her considerable influence and dominance on the field challenged long-standing norms and served as a powerful counter-narrative against the gender biases that sidelined women in design. Ultimately, her images – part blueprints, part paintings – not only proved that architecture could be unapologetically visionary and abstract but also that materializing it is not as impossible as one would think.My Bedroom by Daniel Wing-Hou Ho, A+ Vision Awards, 2023
    Even though paper architecture began as a medium of rebellion against architectural convention in the mid-20th century, it remains, until today, a vital tool for activism and social justice. Operating in the digital age, social media and digital platforms have amplified its reach, also having given it different visual forms such as digital collages, speculative renders, gifs, reels and interactive visual narratives. What was once a flyer, a journal or a newspaper extract, can now be found in open-source repositories, standing against authoritarianism, climate inaction, political violence and systemic inequality.
    Groups such as Forensic Architecture carry out multidisciplinary research, investigating cases of state violence and violations of human rights through rigorous mapping and speculative visualization. Additionally, competitions such as the eVolo Skyscraper or platforms like ArchOutLoud and Design Earth offer opportunities and space for architects to tackle environmental concerns and dramatize the urgency of inaction. Imaginative floating habitats, food cities, biodegradable megastructures etc. instigate debates and conversations through the form of environmental storytelling.
    The Stamper Battery by By William du Toit, A+ Vision Awards, 2023
    Despite being often condemned as “unbuildable”, “impractical” or even “escapist,” paper architecture acts as a counterweight to the discipline’s increasing instrumentalization as merely a functional or commercial enterprise. In architecture schools it is used as a prompt for “thinking differently” and a tool for “critiquing without compromise”. Above all however, paper architecture matters because it keeps architecture ethically alive. It reminds architects to ask the uncomfortable questions: how should we design for environmental sustainability, migrancy or social equality, instead of focusing on profit, convenience and spectacle? Similar to a moral compass or speculative mirror, unbuilt visions can trigger political, social and environmental turns that reshape not just how we build, but why we build at all.
    Architizer’s Vision Awards are back! The global awards program honors the world’s best architectural concepts, ideas and imagery. Submit your work ahead of the Final Entry Deadline on July 11th!
    Featured Image: Into the Void: Fragmented Time, Space, Memory, and Decay in Hiroshima by Victoria Wong, A+ Vision Awards 2023
    The post Paper Architecture: From Soviet Subversion to Zaha’s Suprematism appeared first on Journal.
    #paper #architecture #soviet #subversion #zahas
    Paper Architecture: From Soviet Subversion to Zaha’s Suprematism
    Architizer’s Vision Awards are back! The global awards program honors the world’s best architectural concepts, ideas and imagery. Submit your work ahead of the Final Entry Deadline on July 11th! Behind the term “paper architecture” hides a strange paradox: the radical act of building without, well, building. Paper architecture is usually associated with speculative design projects, presented in the form of drawings, which can also be considered art pieces. However, even though it is often dismissed as a mere utopian or academic exercise, paper architecture has historically served as a powerful form of protest, advocating against political regimes, architectural orthodoxy or cultural stagnation. Unbound by real-world limitations such as materials, regulations and budgets, paper architects are free to focus on the messages behind their designs rather than constantly striving for their implementation. In parallel, due to its subtleness, paper architecture has become a platform that enables radical commentary via a rather “safe” medium. Instead of relying on more traditional forms of protestthis powerful visual language, combined with scrupulous aesthetics and imagination can start a more formidable “behind-the-scenes rebellion”. Unearthing Nostalgia by Bruno Xavier & Michelle Ashley Ovanessians, A+ Vision Awards, 2023 Perhaps the most well-known paper architects, Archigram was a radical British collective that was formed in the 1960s in London. Their work Walking City or Plug-In City showcased visions of a playful, technologically driven architecture that deeply contrasted and, by extent, protested against the rigid regime of post-war modernism and its extensive bureaucracy. This pop-art-style architecture served as a powerful critique towards the saturated idea of functional monotony. Additionally, the Russian architect, artist, and curator, Yuri Avvakumuv introduced the term “paper architecture” within the restrictive cultural and political climate of late Soviet Russia. Having to deal with heavy censorship, Avvakumuv turned to competitions and speculative drawings in an attempt resist that dominance of totalitarian architecture. Poetic, deeply allegorical and oftentimes ironic architectural renderings, critiqued the bureaucratic sterility of Soviet planning and the state-mandated architectural principles architects had to follow. Consequently, this profound demonstration of un-built architecture within the specific setting, turned into a collective cultural wave that advocated artistic autonomy and expression for the built environment. Klothos’ Loom of Memories by Ioana Alexandra Enache, A+ Vision Awards, 2023 The Amerian architect Lebbeus Woods was also one of the most intellectually intense practitioners of paper architecture, whose work touches upon global issues on war zones and urban trauma. His imaginative, post-apocalyptic cities opened up discussions for rebuilding after destruction. Works such as War and Architecture and Underground Berlin, albeit “dystopic”, acted as moral propositions, exploring potential reconstructions that would “heal” these cities. Through his drawings, he rigorously investigated and examined scenarios of ethical rebuilding, refusing to comply to the principles of popular commerce, and instead creating a new architectural practice of political resistance. Finally, operating within a very male-dominated world, Zaha Hadid’s earlier work — particularly on Malevich — served as a protesting tool on multiple levels. Influenced by Suprematist aesthetics, her bold, dynamic compositions stood against the formal conservatism of architectural ideas, where the design must always yield to gravity and function. In parallel, her considerable influence and dominance on the field challenged long-standing norms and served as a powerful counter-narrative against the gender biases that sidelined women in design. Ultimately, her images – part blueprints, part paintings – not only proved that architecture could be unapologetically visionary and abstract but also that materializing it is not as impossible as one would think.My Bedroom by Daniel Wing-Hou Ho, A+ Vision Awards, 2023 Even though paper architecture began as a medium of rebellion against architectural convention in the mid-20th century, it remains, until today, a vital tool for activism and social justice. Operating in the digital age, social media and digital platforms have amplified its reach, also having given it different visual forms such as digital collages, speculative renders, gifs, reels and interactive visual narratives. What was once a flyer, a journal or a newspaper extract, can now be found in open-source repositories, standing against authoritarianism, climate inaction, political violence and systemic inequality. Groups such as Forensic Architecture carry out multidisciplinary research, investigating cases of state violence and violations of human rights through rigorous mapping and speculative visualization. Additionally, competitions such as the eVolo Skyscraper or platforms like ArchOutLoud and Design Earth offer opportunities and space for architects to tackle environmental concerns and dramatize the urgency of inaction. Imaginative floating habitats, food cities, biodegradable megastructures etc. instigate debates and conversations through the form of environmental storytelling. The Stamper Battery by By William du Toit, A+ Vision Awards, 2023 Despite being often condemned as “unbuildable”, “impractical” or even “escapist,” paper architecture acts as a counterweight to the discipline’s increasing instrumentalization as merely a functional or commercial enterprise. In architecture schools it is used as a prompt for “thinking differently” and a tool for “critiquing without compromise”. Above all however, paper architecture matters because it keeps architecture ethically alive. It reminds architects to ask the uncomfortable questions: how should we design for environmental sustainability, migrancy or social equality, instead of focusing on profit, convenience and spectacle? Similar to a moral compass or speculative mirror, unbuilt visions can trigger political, social and environmental turns that reshape not just how we build, but why we build at all. Architizer’s Vision Awards are back! The global awards program honors the world’s best architectural concepts, ideas and imagery. Submit your work ahead of the Final Entry Deadline on July 11th! Featured Image: Into the Void: Fragmented Time, Space, Memory, and Decay in Hiroshima by Victoria Wong, A+ Vision Awards 2023 The post Paper Architecture: From Soviet Subversion to Zaha’s Suprematism appeared first on Journal. #paper #architecture #soviet #subversion #zahas
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    Paper Architecture: From Soviet Subversion to Zaha’s Suprematism
    Architizer’s Vision Awards are back! The global awards program honors the world’s best architectural concepts, ideas and imagery. Submit your work ahead of the Final Entry Deadline on July 11th! Behind the term “paper architecture” hides a strange paradox: the radical act of building without, well, building. Paper architecture is usually associated with speculative design projects, presented in the form of drawings, which can also be considered art pieces. However, even though it is often dismissed as a mere utopian or academic exercise, paper architecture has historically served as a powerful form of protest, advocating against political regimes, architectural orthodoxy or cultural stagnation. Unbound by real-world limitations such as materials, regulations and budgets, paper architects are free to focus on the messages behind their designs rather than constantly striving for their implementation. In parallel, due to its subtleness, paper architecture has become a platform that enables radical commentary via a rather “safe” medium. Instead of relying on more traditional forms of protest (such as strikes or marches) this powerful visual language, combined with scrupulous aesthetics and imagination can start a more formidable “behind-the-scenes rebellion”. Unearthing Nostalgia by Bruno Xavier & Michelle Ashley Ovanessians, A+ Vision Awards, 2023 Perhaps the most well-known paper architects, Archigram was a radical British collective that was formed in the 1960s in London. Their work Walking City or Plug-In City showcased visions of a playful, technologically driven architecture that deeply contrasted and, by extent, protested against the rigid regime of post-war modernism and its extensive bureaucracy. This pop-art-style architecture served as a powerful critique towards the saturated idea of functional monotony. Additionally, the Russian architect, artist, and curator, Yuri Avvakumuv introduced the term “paper architecture” within the restrictive cultural and political climate of late Soviet Russia (1984). Having to deal with heavy censorship, Avvakumuv turned to competitions and speculative drawings in an attempt resist that dominance of totalitarian architecture. Poetic, deeply allegorical and oftentimes ironic architectural renderings, critiqued the bureaucratic sterility of Soviet planning and the state-mandated architectural principles architects had to follow. Consequently, this profound demonstration of un-built architecture within the specific setting, turned into a collective cultural wave that advocated artistic autonomy and expression for the built environment. Klothos’ Loom of Memories by Ioana Alexandra Enache, A+ Vision Awards, 2023 The Amerian architect Lebbeus Woods was also one of the most intellectually intense practitioners of paper architecture, whose work touches upon global issues on war zones and urban trauma. His imaginative, post-apocalyptic cities opened up discussions for rebuilding after destruction. Works such as War and Architecture and Underground Berlin, albeit “dystopic”, acted as moral propositions, exploring potential reconstructions that would “heal” these cities. Through his drawings, he rigorously investigated and examined scenarios of ethical rebuilding, refusing to comply to the principles of popular commerce, and instead creating a new architectural practice of political resistance. Finally, operating within a very male-dominated world, Zaha Hadid’s earlier work — particularly on Malevich — served as a protesting tool on multiple levels. Influenced by Suprematist aesthetics, her bold, dynamic compositions stood against the formal conservatism of architectural ideas, where the design must always yield to gravity and function. In parallel, her considerable influence and dominance on the field challenged long-standing norms and served as a powerful counter-narrative against the gender biases that sidelined women in design. Ultimately, her images – part blueprints, part paintings – not only proved that architecture could be unapologetically visionary and abstract but also that materializing it is not as impossible as one would think. (Your) My Bedroom by Daniel Wing-Hou Ho, A+ Vision Awards, 2023 Even though paper architecture began as a medium of rebellion against architectural convention in the mid-20th century, it remains, until today, a vital tool for activism and social justice. Operating in the digital age, social media and digital platforms have amplified its reach, also having given it different visual forms such as digital collages, speculative renders, gifs, reels and interactive visual narratives. What was once a flyer, a journal or a newspaper extract, can now be found in open-source repositories, standing against authoritarianism, climate inaction, political violence and systemic inequality. Groups such as Forensic Architecture (Goldsmiths, University of London)  carry out multidisciplinary research, investigating cases of state violence and violations of human rights through rigorous mapping and speculative visualization. Additionally, competitions such as the eVolo Skyscraper or platforms like ArchOutLoud and Design Earth offer opportunities and space for architects to tackle environmental concerns and dramatize the urgency of inaction. Imaginative floating habitats, food cities, biodegradable megastructures etc. instigate debates and conversations through the form of environmental storytelling. The Stamper Battery by By William du Toit, A+ Vision Awards, 2023 Despite being often condemned as “unbuildable”, “impractical” or even “escapist,” paper architecture acts as a counterweight to the discipline’s increasing instrumentalization as merely a functional or commercial enterprise. In architecture schools it is used as a prompt for “thinking differently” and a tool for “critiquing without compromise”. Above all however, paper architecture matters because it keeps architecture ethically alive. It reminds architects to ask the uncomfortable questions: how should we design for environmental sustainability, migrancy or social equality, instead of focusing on profit, convenience and spectacle? Similar to a moral compass or speculative mirror, unbuilt visions can trigger political, social and environmental turns that reshape not just how we build, but why we build at all. Architizer’s Vision Awards are back! The global awards program honors the world’s best architectural concepts, ideas and imagery. Submit your work ahead of the Final Entry Deadline on July 11th! Featured Image: Into the Void: Fragmented Time, Space, Memory, and Decay in Hiroshima by Victoria Wong, A+ Vision Awards 2023 The post Paper Architecture: From Soviet Subversion to Zaha’s Suprematism appeared first on Journal.
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