Luke Skywalker's Landspeeder - Star Wars Ultimate Collector Series Lego Set $175 (was $240) See at Amazon See at Walmart One of the best Star Wars Lego vehicle model kits is on sale for a stellar price at Amazon and Walmart. Luke Skywalker's iconic Landspeeder X-34 is discounted to $175 in the retailers' early Black Friday 2024 sales. This 1,890-piece model kit is part of Lego's Ultimate Collector Series, which is a subset of Star Wars Lego sets with more intricate and realistic designs. Ultimate Collector Series Star Wars Lego sets aren't discounted often, so this deal, which slashes $65 from its $240 list price, isn't likely to remain in stock for long.Get deal at AmazonGet deal at WalmartLuke Skywalker's Landspeeder - Lego Star Wars Ultimate Collector Series$175 ($240)Lego XR-34 LandspeederGallery This highly detailed model of Luke's ground-based vehicle measure 19 inches long once fully assembled. While George Lucas and his film crew needed reflective material, gelatin on the camera lens, and shadow effects to create the illusion that the landspeeder was hovering, you'll be able to achieve the same effect with a display stand. It also comes with a plaque with key details about the Landspeeder and two Star Wars minifigures: Luke Skywalker (naturally) and C-3PO. They can stand next to the plaque or sit in the cockpit.Continue Reading at GameSpot

The Binding of Isaac: Four Souls - Ultimate Collector's Box $45 (was $125) See at Amazon The Binding of Isaac: Four Souls Base Set $16.49 (was $45) See at Amazon Amazon has a lot of great board and card game deals ahead of Black Friday 2024, including numerous fun tabletop experiences adapted from video games. Not everything that's on sale could be described as a true bargain, but The Binding of Isaac: Four Souls absolutely is at its current prices. The strategy card game's core set is only $16.49, down from $45. But it gets even better, as The Binding of Isaac: Four Souls Ultimate Collector's Box is on sale for $45, which is $80 less than its $125 list price. The Ultimate Collector's Box includes over 700 illustrated cards across the core set and two expansions bundled with it.These deals were offered in the lead-up to Amazon's Big Deal Days event in October, but it's cool to see them return in the lead-up to the retailer's Black Friday 2024 sale, which begins November 21. And this time you don't need to be a Prime member to get these deals. While that's great news for those without a membership, it also means the Black Friday offers will probably sell out faster than the last round.Continue Reading at GameSpot

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Having multiple playable characters is a common feature in gaming, serving various purposes. Some games use multiple characters to provide different perspectives on events, adding depth to the narrative. Others introduce multiple playable characters to prioritize cooperative play, offering players the chance to team up with friends. Horror games, in particular, have embraced this feature, often including two or more playable characters. This approach has been significantly popularized by developers like Supermassive Games, where characters can meet unexpected deaths based on player choices, creating extra tension.

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Whether theyre looking at nanoscale electron behaviors or starry galaxies colliding millions of light years away, many scientists share a common challenge they must comb through petabytes of data to extract insights that can advance their fields.With the NVIDIA cuPyNumeric accelerated computing library, researchers can now take their data-crunching Python code and effortlessly run it on CPU-based laptops and GPU-accelerated workstations, cloud servers or massive supercomputers. The faster they can work through their data, the quicker they can make decisions about promising data points, trends worth investigating and adjustments to their experiments.To make the leap to accelerated computing, researchers dont need expertise in computer science. They can simply write code using the familiar NumPy interface or apply cuPyNumeric to existing code, following best practices for performance and scalability.Once cuPyNumeric is applied, they can run their code on one or thousands of GPUs with zero code changes.The latest version of cuPyNumeric, now available on Conda and GitHub, offers support for the NVIDIA GH200 Grace Hopper Superchip, automatic resource configuration at run time and improved memory scaling. It also supports HDF5, a popular file format in the scientific community that helps efficiently manage large, complex data.Researchers at the SLAC National Accelerator Laboratory, Los Alamos National Laboratory, Australia National University, UMass Boston, the Center for Turbulence Research at Stanford University and the National Payments Corporation of India are among those who have integrated cuPyNumeric to achieve significant improvements in their data analysis workflows.Less Is More: Limitless GPU Scalability Without Code ChangesPython is the most common programming language for data science, machine learning and numerical computing, used by millions of researchers in scientific fields including astronomy, drug discovery, materials science and nuclear physics. Tens of thousands of packages on GitHub depend on the NumPy math and matrix library, which had over 300 million downloads last month. All of these applications could benefit from accelerated computing with cuPyNumeric.Many of these scientists build programs that use NumPy and run on a single CPU-only node limiting the throughput of their algorithms to crunch through increasingly large datasets collected by instruments like electron microscopes, particle colliders and radio telescopes.cuPyNumeric helps researchers keep pace with the growing size and complexity of their datasets by providing a drop-in replacement for NumPy that can scale to thousands of GPUs. cuPyNumeric doesnt require code changes when scaling from a single GPU to a whole supercomputer. This makes it easy for researchers to run their analyses on accelerated computing systems of any size.Solving the Big Data Problem, Accelerating Scientific DiscoveryResearchers at SLAC National Accelerator Laboratory, a U.S. Department of Energy lab operated by Stanford University, have found that cuPyNumeric helps them speed up X-ray experiments conducted at the Linac Coherent Light Source.A SLAC team focused on materials science discovery for semiconductors found that cuPyNumeric accelerated its data analysis application by 6x, decreasing run time from minutes to seconds. This speedup allows the team to run important analyses in parallel when conducting experiments at this highly specialized facility.By using experiment hours more efficiently, the team anticipates it will be able to discover new material properties, share results and publish work more quickly.Other institutions using cuPyNumeric include:Australia National University, where researchers used cuPyNumeric to scale the Levenberg-Marquardt optimization algorithm to run on multi-GPU systems at the countrys National Computational Infrastructure. While the algorithm can be used for many applications, the researchers initial target is large-scale climate and weather models.Los Alamos National Laboratory, where researchers are applying cuPyNumeric to accelerate data science, computational science and machine learning algorithms. cuPyNumeric will provide them with additional tools to effectively use the recently launched Venado supercomputer, which features over 2,500 NVIDIA GH200 Grace Hopper Superchips.Stanford Universitys Center for Turbulence Research, where researchers are developing Python-based computational fluid dynamics solvers that can run at scale on large accelerated computing clusters using cuPyNumeric. These solvers can seamlessly integrate large collections of fluid simulations with popular machine learning libraries like PyTorch, enabling complex applications including online training and reinforcement learning.UMass Boston, where a research team is accelerating linear algebra calculations to analyze microscopy videos and determine the energy dissipated by active materials. The team used cuPyNumeric to decompose a matrix of 16 million rows and 4,000 columns.National Payments Corporation of India, the organization behind a real-time digital payment system used by around 250 million Indians daily and expanding globally. NPCI uses complex matrix calculations to track transaction paths between payers and payees. With current methods, it takes about 5 hours to process data for a one-week transaction window on CPU systems. A trial showed that applying cuPyNumeric to accelerate the calculations on multi-node NVIDIA DGX systems could speed up matrix multiplication by 50x, enabling NPCI to process larger transaction windows in less than an hour and detect suspected money laundering in near real time.To learn more about cuPyNumeric, see a live demo in the NVIDIA booth at the Supercomputing 2024 conference in Atlanta, join the theater talk in the expo hall and participate in the cuPyNumeric workshop. Watch the NVIDIA special address at SC24.

Since its introduction, the NVIDIA Hopper architecture has transformed the AI and high-performance computing (HPC) landscape, helping enterprises, researchers and developers tackle the worlds most complex challenges with higher performance and greater energy efficiency.During the Supercomputing 2024 conference, NVIDIA announced the availability of the NVIDIA H200 NVL PCIe GPU the latest addition to the Hopper family. H200 NVL is ideal for organizations with data centers looking for lower-power, air-cooled enterprise rack designs with flexible configurations to deliver acceleration for every AI and HPC workload, regardless of size.According to a recent survey, roughly 70% of enterprise racks are 20kW and below and use air cooling. This makes PCIe GPUs essential, as they provide granularity of node deployment, whether using one, two, four or eight GPUs enabling data centers to pack more computing power into smaller spaces. Companies can then use their existing racks and select the number of GPUs that best suits their needs.Enterprises can use H200 NVL to accelerate AI and HPC applications, while also improving energy efficiency through reduced power consumption. With a 1.5x memory increase and 1.2x bandwidth increase over NVIDIA H100 NVL, companies can use H200 NVL to fine-tune LLMs within a few hours and deliver up to 1.7x faster inference performance. For HPC workloads, performance is boosted up to 1.3x over H100 NVL and 2.5x over the NVIDIA Ampere architecture generation.Complementing the raw power of the H200 NVL is NVIDIA NVLink technology. The latest generation of NVLink provides GPU-to-GPU communication 7x faster than fifth-generation PCIe delivering higher performance to meet the needs of HPC, large language model inference and fine-tuning.The NVIDIA H200 NVL is paired with powerful software tools that enable enterprises to accelerate applications from AI to HPC. It comes with a five-year subscription for NVIDIA AI Enterprise, a cloud-native software platform for the development and deployment of production AI. NVIDIA AI Enterprise includes NVIDIA NIM microservices for the secure, reliable deployment of high-performance AI model inference.Companies Tapping Into Power of H200 NVLWith H200 NVL, NVIDIA provides enterprises with a full-stack platform to develop and deploy their AI and HPC workloads.Customers are seeing significant impact for multiple AI and HPC use cases across industries, such as visual AI agents and chatbots for customer service, trading algorithms for finance, medical imaging for improved anomaly detection in healthcare, pattern recognition for manufacturing, and seismic imaging for federal science organizations.Dropbox is harnessing NVIDIA accelerated computing for its services and infrastructure.Dropbox handles large amounts of content, requiring advanced AI and machine learning capabilities, said Ali Zafar, VP of Infrastructure at Dropbox. Were exploring H200 NVL to continually improve our services and bring more value to our customers.The University of New Mexico has been using NVIDIA accelerated computing in various research and academic applications.As a public research university, our commitment to AI enables the university to be on the forefront of scientific and technological advancements, said Prof. Patrick Bridges, director of the UNM Center for Advanced Research Computing. As we shift to H200 NVL, well be able to accelerate a variety of applications, including data science initiatives, bioinformatics and genomics research, physics and astronomy simulations, climate modeling and more.H200 NVL Available Across EcosystemDell Technologies, Hewlett Packard Enterprise, Lenovo and Supermicro are expected to deliver a wide range of configurations supporting H200 NVL.Additionally, H200 NVL will be available in platforms from Aivres, ASRock Rack, ASUS, GIGABYTE, Ingrasys, Inventec, MSI, Pegatron, QCT, Wistron and Wiwynn.Some systems are based on the NVIDIA MGX modular architecture, which enables computer makers to quickly and cost-effectively build a vast array of data center infrastructure designs.Platforms with H200 NVL will be available from NVIDIAs global systems partners beginning in December. To complement availability from leading global partners, NVIDIA is also developing an Enterprise Reference Architecture for H200 NVL systems.The reference architecture will incorporate NVIDIAs expertise and design principles, so partners and customers can design and deploy high-performance AI infrastructure based on H200 NVL at scale. This includes full-stack hardware and software recommendations, with detailed guidance on optimal server, cluster and network configurations. Networking is optimized for the highest performance with the NVIDIA Spectrum-X Ethernet platform.NVIDIA technologies will be showcased on the showroom floor at SC24, taking place at the Georgia World Congress Center through Nov. 22. To learn more, watch NVIDIAs special address.See notice regarding software product information.