The Archive

Nvidia disputes NYT report that CEO Jensen Huang was investigated as Chinese intelligence target.

Report: Trump administration took no action on intelligence of alleged Chinese spy near Nvidia CEO.

Alleged Chinese intelligence operative had personal connection to Nvidia CEO Jensen Huang; geopolitical/legal news unrelated to AI research or engineering.

U.S. investigates alleged Chinese operative at Singapore AI cloud firm for illegal Nvidia chip sales to China.

NVIDIA Nemotron 3.5 Super VL is a 120B-parameter mixture-of-experts model that pairs reasoning and tool use with multimodal understanding. This post explores its architecture and use cases, then shows how to deploy it on Amazon EKS, EKS Auto Mode, and Amazon SageMaker HyperPod EKS with NVIDIA Dynamo and vLLM.

Iluvatar CoreX's Tiangai 300 GPU claims to match or exceed Nvidia H200 in multimodal AI tasks.

Nvidia releases Nemotron 3.5 Transcribe, open-weights ASR model for 40 languages.

Nvidia ships open-source speech-to-text model at Whisper scale supporting 40 languages for batch processing.

Many AI workloads need only a fraction of a GPU. Giving each one an entire device can leave capacity idle; sharing GPUs lets platform teams serve more workloads... Many AI workloads need only a fraction of a GPU. Giving each one an entire device can leave capacity idle; sharing GPUs lets platform teams serve more workloads with the infrastructure they already have. NVIDIA GPUs already support several approaches to sharing. Multi-instance GPU (MIG) splits a GPU into hardware-isolated instances that are transparent to the application… Source

NVIDIA RTX Spark is a new Windows PC platform for AI development, creation, and gaming in slim laptops and compact desktops. Powered by an NVIDIA Blackwell RTX... NVIDIA RTX Spark is a new Windows PC platform for AI development, creation, and gaming in slim laptops and compact desktops. Powered by an NVIDIA Blackwell RTX GPU and NVIDIA Grace CPU, it combines up to 128 GB of unified memory with the full NVIDIA CUDA stack and RTX technologies including ray tracing and DLSS 5. This post covers the following technologies for game developers building on RTX… Source

A Siemens report estimates that unplanned downtime costs an average large plant in the surveyed sectors $253 million per year. Whether investigating a failed... A Siemens report estimates that unplanned downtime costs an average large plant in the surveyed sectors $253 million per year. Whether investigating a failed circuit board or responding to a rush order, factory teams must gather evidence across systems, consult specialists, and coordinate the next step. The data may be available, but turning it into a response takes substantial time and effort. Source

As AI adoption expands globally, people across languages and cultures are using it in more aspects of their daily lives. General-purpose language models serve... As AI adoption expands globally, people across languages and cultures are using it in more aspects of their daily lives. General-purpose language models serve as a powerful foundation, but they sometimes lack the local knowledge, cultural nuance, and precision a particular application needs. Post-training can adapt an open model to these needs. With suitable data and evaluation… Source

Generating tokens faster starts with identifying what limits inference performance. In the batch-size-1 evaluation of Ling-3.0-flash from Inclusion AI on NVIDIA... Generating tokens faster starts with identifying what limits inference performance. In the batch-size-1 evaluation of Ling-3.0-flash from Inclusion AI on NVIDIA DGX Spark, profiling showed that decoding was memory-bound: reading model weights drove execution time. Two operations stood out—the mixture-of-experts (MoE) computation and the output-layer projection that produces scores across the model’… Source

General Compute raises $100M Series A backing alternative inference chips outside Nvidia ecosystem.

Simplismart, Indian startup optimizing GPU efficiency, grows 14x ARR with U.S. dominance; Nvidia backs infrastructure play.

Nvidia's Mark Weatherford argues CISOs must guide business risk-weighting for AI deployments and ensure ownership of deployment consequences.

At the 2026 Open Compute Project (OCP) Global Summit, Meta shared the networking innovations that are directly supporting our latest AI models, including Muse Wonder and Muse Spark, and our gigawatt-scale Prometheus cluster. Highlights IcePack & IceCube: Meta’s first liquid-cooled network rack with 102.4T switch blades (128×800G, Broadcom Tomahawk 6C and NVIDIA Spectrum-6 variants), running [...] Read More... The post Meta Networking at the 2026 OCP Global Summit: Open Innovation for Scale-Up, Scale-Out, and Scale-Across AI Networks appeared first on Engineering at Meta.

AI Factories are some of the most technologically complex operational environments ever created. Hundreds of thousands of GPUs, NVIDIA Spectrum-X Ethernet... AI Factories are some of the most technologically complex operational environments ever created. Hundreds of thousands of GPUs, NVIDIA Spectrum-X Ethernet scale-out, scale-across and scale-in fabrics, NVIDIA NVLink scale-up fabrics, distributed schedulers, and multi-week training runs operate in continuous lockstep.These layers function as one production system even when different teams own their… Source

Training large-scale AI models on thousands of GPUs is a high-stakes operation where monitoring and automation must be built-in to optimize cluster resiliency... Training large-scale AI models on thousands of GPUs is a high-stakes operation where monitoring and automation must be built-in to optimize cluster resiliency and uptime at scale. This concept isn’t new to data center operators who have measured mean-time-to-repair (MTTR) and mean-time-between-failure (MTBF) long before AI factories existed. Now the stakes are higher. Modern AI factories run… Source

Alibaba and Nvidia executives pitch Europe on open-source AI models and industrial automation applications.

Nvidia CEO demonstrates Microduck toy robots with voice-command capability; 20,000 units sold.

The Global Performance Tuner is a feature for automatic performance tuning released in NVIDIA TensorRT 11.1. It provides automatic, accuracy-aware and... The Global Performance Tuner is a feature for automatic performance tuning released in NVIDIA TensorRT 11.1. It provides automatic, accuracy-aware and end-to-end performance tuning within TensorRT by customizing different optimizations for different models. For more details, see Part 1 of this series, which introduces the challenges associated with scheduling (a critical stage in deep learning… Source

Deploying a trained neural network involves more than translating each operator into a GPU kernel. It is a compilation problem with many legitimate answers. For... Deploying a trained neural network involves more than translating each operator into a GPU kernel. It is a compilation problem with many legitimate answers. For example, when NVIDIA TensorRT builds an engine, it makes interdependent decisions about graph-IR transformations, fusion, implementation selection, memory placement, and quantization. This process is called scheduling. Source

inDrive plans to begin testing Pony.ai's robotaxis in Kazakhstan's capital in the first half of 2027.

Mars not depicted. | Image: Cath Virginia / The Verge, Getty Images Oh sure, SpaceX's credit risk is pretty high because it's about to borrow another $40 billion to spend on Nvidia chips, but who cares about that? SpaceX and Tesla have formed special committees to discuss a merger! The degree to which you find this merger compelling likely has to do with the degree to which you believe Elon Musk about wanting to go to Mars, or "spread the light of consciousness," as he put it in this week's SpaceX earnings call (and earlier S-1). Now, a reasonably informed person knows that we don't have the ...

GPU applications are processing ever-growing datasets with increasingly powerful accelerators. According to the 2025 Stanford AI Index, compute for notable AI... GPU applications are processing ever-growing datasets with increasingly powerful accelerators. According to the 2025 Stanford AI Index, compute for notable AI models has been doubling approximately every five months, while the size of datasets used to train LLMs has been doubling approximately every eight months. As computation and data scale together, moving data efficiently from storage becomes… Source

Empirical evaluation of LLM inference optimizations (KV-cache pruning, speculative decoding, quantization) on Apple Silicon vs NVIDIA GPUs.

AI developers aren’t just running a single model anymore—they’re orchestrating multiple models in parallel, chaining inference calls, and experimenting... AI developers aren’t just running a single model anymore—they’re orchestrating multiple models in parallel, chaining inference calls, and experimenting with frontier-class architectures that demand hundreds of gigabytes of memory. NVIDIA DGX Spark was built to bring AI development to the desk, and now you’ll be able to link even more Spark nodes together. At GTC Berlin… Source

It was a busy summer for AI lobbyists. Anthropic, OpenAI, and Nvidia each had their highest-spending lobbying quarter between July and September, federal disclosures show. Nvidia spent the most of the group at $2.2 million, followed by Anthropic's $2.1 million and OpenAI's $1.5 million. Compared to other Big Tech companies with long-established presences in Washington, the sums aren't out of the ordinary. Microsoft, for example, has spent at least $2 million a quarter going back to 2021, and Google regularly spends over $3 million, including this quarter. But it's a clear signal that the thre...