Speedata, a Tel Aviv-based startup developing an analytics processing unit (APU) designed to accelerate big data analytic and AI workloads, has raised a $44M Series B funding round, bringing its total capital raised to $114M. The Series B round was l

Speedata, a Tel Aviv-based startup developing an analytics processing unit (APU) designed to accelerate big data analytic and AI workloads, has raised a $44M Series B funding round, bringing its total capital raised to $114M. The Series B round was l

The issues don't stop for RTX 50-series owners.

NVIDIA’s New AI: From Video Games to Reality!

In context: TSMC has steadily raised the prices of its most advanced semiconductor process nodes over the past several years – so much so that one analysis suggests the cost per transistor hasn't decreased in over a decade. Further price hikes, driven by tariffs and rising development costs, are reinforcing the notion that Moore's Law is truly dead. The Commercial Times reports that TSMC's upcoming N2 2nm semiconductors will cost $30,000 per wafer, a roughly 66% increase over the company's 3nm chips. Future nodes are expected to be even more expensive and likely reserved for the largest manufacturers. TSMC has justified these price increases by citing the massive cost of building 2nm fabrication plants, which can reach up to $725 million. According to United Daily News, major players such as Apple, AMD, Qualcomm, Broadcom, and Nvidia are expected to place orders before the end of the year despite the higher prices, potentially bringing TSMC's 2nm Arizona fab to full capacity. Also see: How profitable are TSMC's nodes: crunching the numbers Unsurprisingly, Apple is getting first dibs. The A20 processor in next year's iPhone 18 Pro is expected to be the first chip based on TSMC's N2 process. Intel's Nova Lake processors, targeting desktops and possibly high-end laptops, are also slated to use N2 and are expected to launch next year. Earlier reports indicated that yield rates for TSMC's 2nm process reached 60% last year and have since improved. New data suggests that 256Mb SRAM yield rates now exceed 90%. Trial production is likely already underway, with mass production scheduled to begin later this year. // Related Stories With tape-outs for 2nm-based designs surpassing previous nodes at the same development stage, TSMC aims to produce tens of thousands of wafers by the end of 2025. TSMC also plans to follow N2 with N2P and N2X in the second half of next year. N2P is expected to offer an 18% performance boost over N3E at the same power level and 36% greater energy efficiency at the same speed, along with significantly higher logic density. N2X, slated for mass production in 2027, will increase maximum clock frequencies by 10%. As semiconductor geometries continue to shrink, power leakage becomes a major concern. TSMC's 2nm nodes will address this issue with gate-all-around (GAA) transistor architectures, enabling more precise control of electrical currents. Beyond 2nm lies the Angstrom era, where TSMC will implement backside power delivery to further enhance performance. Future process nodes like A16 (1.6nm) and A14 (1.4nm) could cost up to $45,000 per wafer. Meanwhile, Intel is aiming to outpace TSMC's roadmap. The company recently began risk production of its A18 node, which also features gate-all-around and backside power delivery. These chips are expected to debut later this year in Intel's upcoming laptop CPUs, codenamed Panther Lake.

Key Takeaways Dell and Nvidia will together provide architecture for the next set of supercomputers, named Doudna, for the US Department of Energy. Dell will focus more on sustainable hardware and cooling systems, while Nvidia will provide its AI architecture, including the Vera Rubin AI chips. The US Department of Energy wants to focus more on the sustainable development of AI, hence the choice of environment-conscious companies. The US Department of Energy said that Dell’s next batch of supercomputers will be delivered with Nvidia’s ‘Vera Rubin’ AI chips, marking the beginning of a new era of AI dominance in research. The said systems are expected to be 10x faster than the current batch of supercomputers, which HP provided. The supercomputer will be named ‘Doudna,’ after Jennifer Doudna, a Nobel Prize winner who made key contributions in CRISPR gene-editing. Supercomputers have been instrumental in key scientific discoveries in the last few decades and also played a big role in the design and maintenance of the U.S. nuclear weapons arsenal. And now, with the introduction of artificial intelligence, we’re heading towards a new decade of faster and more efficient scientific research. It (supercomputers) is the foundation of scientific discovery for our country. It is also a foundation for economic and technological leadership. And with that, national security – Nvidia CEO, Jensen Huang Dell Going All in on AI This isn’t the first time Dell and Nvidia have come together to develop newer AI solutions. Back in March 2024, Dell announced the Dell AI Factory with NVIDIA, an end-to-end enterprise AI solution designed for businesses.  This joint venture used Dell’s infrastructure, such as servers, storage, and networking, combined with NVIDIA’s AI architecture and technologies such as GPUs, DPUs, and AI software. Image Credit – Dell For instance, the Dell PowerEdge server uses NVIDIA’s full AI stack to provide enterprises with solutions required for a wide range of AI applications, including speech recognition, cybersecurity, recommendation systems, and language-based services. The demand for Dell’s AI servers has also increased, reaching $12.1B in the first quarter of 2025, with a total backlog of $14.4B, which suggests a strong future demand and order book. The company has set a bold profit forecast between $28.5B and $29.5B, as against the analysts’ prediction of $ 25.05 B. With Doudna, Dell is well-positioned to lead the next generation of supercomputers in AI research, invention, and discoveries. Focus on Energy Efficiency Seagate has warned about the unprecedented increase in demand for AI data storage in the coming few years, which is a significant challenge to the sustainability of AI data centers. Global data volume is expected to increase threefold by 2028.  Image Credit – DIGITIMES Asian The data storage industry currently produces only 1-2 zettabytes (1 zettabyte equals 1 trillion gigabytes) of storage annually, which is much lower than what would be required in the next 4-5 years. At the same time, Goldman Sachs predicts that power requirements will also go up by 165% by 2030 due to increasing demand for AI data centers. This calls for a more sustainable approach for the supercomputing industry as well.  Dell will use its proprietary technologies, such as Direct Liquid Cooling, the PowerCool eRDHx, and Smart Flow design in the Doudna, ensuring energy efficiency. Direct Liquid Cooling (DLC) increased computing density by supporting more cores per rack, which reduces cooling costs by as much as 45%. Dell’s PowerCool eRDHx is a self-contained airflow design that can capture 100% of the heat generated by IT systems. This reduces the dependency on expensive chillers, as eRDHx can work in usual temperatures of 32 and 36 degrees Celsius, leading to 60% savings in cooling energy costs. Lastly, the Dell Smart Flow design improves airflow within IT components and reduces the fan power by 52%. This leads to better performance with fewer cooling requirements. Besides this, Dell plans to incorporate Leak Sense Technology. If a coolant leak occurs, the system’s leak sensor will log an alert in the iDRAC system so that swift action can be taken. As per a report titled ‘Energy and AI’ by the IEA, the data center electricity demand will increase to 945 terawatt-hours (TWh) by 2030. For comparison, this is more than the total electricity consumption of Japan today. The US alone will consume more electricity in 2030 for processing data than for manufacturing all energy-intensive goods combined, including aluminum, steel, cement, and chemicals. Therefore, the need to develop sustainable AI data centers and supercomputers cannot be highlighted enough. Dell’s technology-focused, sustainable approach can be a pivotal point in how efficiently we use AI in the next decade. The US Department of Energy’s choice of Dell also seems to be a conscious move to shift towards companies that give importance to sustainability and can vouch for the long-term viability of research-intensive AI setups. Krishi is a seasoned tech journalist with over four years of experience writing about PC hardware, consumer technology, and artificial intelligence.  Clarity and accessibility are at the core of Krishi’s writing style. He believes technology writing should empower readers—not confuse them—and he’s committed to ensuring his content is always easy to understand without sacrificing accuracy or depth. Over the years, Krishi has contributed to some of the most reputable names in the industry, including Techopedia, TechRadar, and Tom’s Guide. A man of many talents, Krishi has also proven his mettle as a crypto writer, tackling complex topics with both ease and zeal. His work spans various formats—from in-depth explainers and news coverage to feature pieces and buying guides.  Behind the scenes, Krishi operates from a dual-monitor setup (including a 29-inch LG UltraWide) that’s always buzzing with news feeds, technical documentation, and research notes, as well as the occasional gaming sessions that keep him fresh.  Krishi thrives on staying current, always ready to dive into the latest announcements, industry shifts, and their far-reaching impacts.  When he's not deep into research on the latest PC hardware news, Krishi would love to chat with you about day trading and the financial markets—oh! And cricket, as well. View all articles by Krishi Chowdhary Our editorial process The Tech Report editorial policy is centered on providing helpful, accurate content that offers real value to our readers. We only work with experienced writers who have specific knowledge in the topics they cover, including latest developments in technology, online privacy, cryptocurrencies, software, and more. Our editorial policy ensures that each topic is researched and curated by our in-house editors. We maintain rigorous journalistic standards, and every article is 100% written by real authors.

Editor’s note: This blog, originally published on October 28, 2024, has been updated. Financial losses from worldwide credit card transaction fraud are projected to reach more than $403 billion over the next decade. The new NVIDIA AI Blueprint for financial fraud detection can help combat this burgeoning epidemic — using accelerated data processing and advanced algorithms to improve AI’s ability to detect and prevent credit card transaction fraud. Launched this week at the Money20/20 financial services conference, the blueprint provides a reference example for financial institutions to identify subtle patterns and anomalies in transaction data based on user behavior to improve accuracy and reduce false positives compared with traditional methods. It shows developers how to build a financial fraud detection workflow by providing reference code, deployment tools and a reference architecture. Companies can streamline the migration of their fraud detection workflows from traditional compute to accelerated compute using the NVIDIA AI Enterprise software platform and NVIDIA accelerated computing. The NVIDIA AI Blueprint is available for customers to run on Amazon Web Services, with availability coming soon on Dell Technologies and Hewlett Packard Enterprise. Customers can also use the blueprint through service offerings from NVIDIA partners including Cloudera, EXL, Infosys and SHI International. Businesses embracing comprehensive machine learning (ML) tools and strategies can observe up to an estimated 40% improvement in fraud detection accuracy, boosting their ability to identify and stop fraudsters faster and mitigate harm. As such, leading financial organizations like American Express and Capital One have been using AI to build proprietary solutions that mitigate fraud and enhance customer protection. The new AI Blueprint accelerates model training and inference, and demonstrates how these components can be wrapped into a single, easy-to-use software offering, powered by NVIDIA AI. Currently optimized for credit card transaction fraud, the blueprint could be adapted for use cases such as new account fraud, account takeover and money laundering. Using Accelerated Computing and Graph Neural Networks for Fraud Detection Traditional data science pipelines lack the compute acceleration to handle the massive data volumes required for effective fraud detection. ML models like XGBoost are effective for detecting anomalies in individual transactions but fall short when fraud involves complex networks of linked accounts and devices. Helping address these gaps, NVIDIA RAPIDS — part of the NVIDIA CUDA-X collection of microservices, libraries, tools and technologies — enables payment companies to speed up data processing and transform raw data into powerful features at scale. These companies can fuel their AI models and integrate them with graph neural networks (GNNs) to uncover hidden, large-scale fraud patterns by analyzing relationships across different transactions, users and devices. The use of gradient-boosted decision trees — a type of ML algorithm — tapping into libraries such as XGBoost, has long been the standard for fraud detection. The new AI Blueprint for financial fraud detection enhances the XGBoost ML model with NVIDIA CUDA-X Data Science libraries including GNNs to generate embeddings that can be used as additional features to help reduce false positives. The GNN embeddings are fed into XGBoost to create and train a model that can then be orchestrated. In addition, NVIDIA Dynamo-Triton, formerly NVIDIA Triton Inference Server, boosts real-time inferencing while optimizing AI model throughput, latency and utilization. NVIDIA CUDA-X Data Science and Dynamo-Triton are included with NVIDIA AI Enterprise. Leading Financial Services Organizations Adopt AI During a time when many large North American financial institutions are reporting online or mobile fraud losses continue to increase, AI is helping to combat this trend. American Express, which began using AI to fight fraud in 2010, leverages fraud detection algorithms to monitor all customer transactions globally in real time, generating fraud decisions in just milliseconds. Using a combination of advanced algorithms, one of which tapped into the NVIDIA AI platform, American Express enhanced model accuracy, advancing the company’s ability to better fight fraud. European digital bank bunq uses generative AI and large language models to help detect fraud and money laundering. Its AI-powered transaction-monitoring system achieved nearly 100x faster model training speeds with NVIDIA accelerated computing. BNY announced in March 2024 that it became the first major bank to deploy an NVIDIA DGX SuperPOD with DGX H100 systems, which will help build solutions that support fraud detection and other use cases. And now, systems integrators, software vendors and cloud service providers can integrate the new NVIDIA blueprint for fraud detection to boost their financial services applications and help keep customers’ money, identities and digital accounts safe. Explore the NVIDIA AI Blueprint for financial fraud detection and read this NVIDIA Technical Blog on supercharging fraud detection with GNNs. Learn more about AI for fraud detection by visiting the AI Summit at Money20/20, running this week in Amsterdam. See notice regarding software product information.

There's a new GPU champion in town.

We could be seeing Nvidia enter the handheld gaming PC market space with a new gaming SoC, and a new rumor further hints at the possibility.