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Wednesday

  1. Hugging Face

    NVIDIA Releases SimReady Central, an Open Library of Simulation-Ready 3D Assets, on Hugging Face

    NVIDIA has launched SimReady Central on Hugging Face, an open 3D asset library for robot and AI factory simulation. The assets are in OpenUSD format with simulation attributes such as mass, collision geometry, and friction. OEMs can use the SimReady Central submission skill to validate and submit CAD equipment models according to the SimReady configuration, while developers and AI agents can use NVIDIA USD Search to retrieve assets in natural language and assemble them into Isaac Sim scenes. The article uses a pick-and-place scenario with a FANUC robotic arm equipped with a Robotiq gripper and an AI factory layout comparison as examples to demonstrate the workflow of generating scene assembly and validation code with GPT-6 Astra.

    NVIDIA has launched SimReady Central on Hugging Face, an open 3D asset library for robot and AI factory simulation. The assets are in OpenUSD format with simulation attributes such as mass, collision geometry, and friction. OEMs can use the SimReady Central submission skill to validate and submit CAD equipment models according to the SimReady configuration, while developers and AI agents can use NVIDIA USD Search to retrieve assets in natural language and assemble them into Isaac Sim scenes. The

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  2. NVIDIA:Newsroom

    NVIDIA Confidential Computing Adds Confidential Bare Metal Deployment Option

    NVIDIA is expanding its confidential computing capabilities with the launch of a Confidential Bare Metal deployment option, enabling frontier model builders to retain control over their models and data when running sensitive workloads on third-party dedicated infrastructure. This offering, together with the existing Confidential Containers and Confidential Virtual Machines, forms three deployment forms, and adds controls such as data protection in transit, platform integrity measurement, and hardware-level access restrictions. Cohere plans to integrate it into Cohere Model Vault, Google Cloud will support the capability on A4X Max instances based on GB300 NVL72, and providers such as CoreWeave, Microsoft Azure Infrastructure, and Nebius also support related deployments.

    NVIDIA is expanding its confidential computing capabilities with the launch of a Confidential Bare Metal deployment option, enabling frontier model builders to retain control over their models and data when running sensitive workloads on third-party dedicated infrastructure. This offering, together with the existing Confidential Containers and Confidential Virtual Machines, forms three deployment forms, and adds controls such as data protection in transit, platform integrity measurement, and har

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  3. NVIDIA Developer · Generative AI

    NVIDIA Releases OpenShell Reference Architecture to Provide Security and Governance for Large-Scale AI Agent Workloads

    NVIDIA has released the OpenShell reference architecture, using an open-source runtime to treat agent workloads as untrusted objects and grant access on demand. The architecture consists of four components: Gateway, Supervisor, Sandbox, and Policy Prover, which are responsible for sandbox lifecycle and policy management, credential access brokering, workload isolation, and formal verification of policy permissions, respectively. CoreWeave's measurements show that on Vera CPUs, sandbox startup is more than 3 times faster than on x86, agent loops complete 1.8 times faster, and a single rack can support more than 11,000 concurrent agent environments.

    A comparison of NVIDIA Vera (green) against x86 (gray). Left chart shows sandbox startup speed in sandboxes per second, with Vera more than 3x higher than x86. Right chart shows per-task relative performance across 20 agentic loop tasks, with Vera bars consistently above the x86 baseline of 1.0, reaching up to 2x on tasks such as cobol-modernization, fix-git, db-wal-recovery, and openssl-selfsigned-cert. Geometric mean advantage: 1.8x.

    NVIDIA has released the OpenShell reference architecture, using an open-source runtime to treat agent workloads as untrusted objects and grant access on demand. The architecture consists of four components: Gateway, Supervisor, Sandbox, and Policy Prover, which are responsible for sandbox lifecycle and policy management, credential access brokering, workload isolation, and formal verification of policy permissions, respectively. CoreWeave's measurements show that on Vera CPUs, sandbox startup is

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  4. NVIDIA Blog

    GTC Berlin 2026: Live Updates from NVIDIA CEO Jensen Huang's Keynote

    In his keynote at GTC Berlin 2026, NVIDIA founder and CEO Jensen Huang said that European companies such as L'Oréal, Revolut, Spotify, Merck, Vodafone, Zara, Porsche, Nestlé and HSBC are all working with NVIDIA to advance AI. He revealed that NVIDIA has invested nearly $10 billion in Europe's technology ecosystem and collaborates with more than 12,000 partners. Huang described AI as a five-layer structure consisting of energy, AI factories, models and applications, and said we have reached the starting point of a new industrial revolution.

    In his keynote at GTC Berlin 2026, NVIDIA founder and CEO Jensen Huang said that European companies such as L'Oréal, Revolut, Spotify, Merck, Vodafone, Zara, Porsche, Nestlé and HSBC are all working with NVIDIA to advance AI. He revealed that NVIDIA has invested nearly $10 billion in Europe's technology ecosystem and collaborates with more than 12,000 partners. Huang described AI as a five-layer structure consisting of energy, AI factories, models and applications, and said we have reached the s

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  5. Hugging FaceSelected

    NVIDIA Releases Cosmos3-H-Dreams Surgical Generative Simulation Model

    NVIDIA has released Cosmos3-H-Dreams, specialized from the 16-billion-parameter Cosmos3-H-Embodied-Nano for action-conditioned video generation of cholecystectomy. The model is trained with 28 action dimensions, 832 x 480 video, and 10 Hz action alignment, and distillation yields two student checkpoints, four-step and two-step; the two-step version is trained separately, offering faster inference but lower image quality. NVIDIA also provides the FlashDreams streaming runtime and methods for adapting to custom data and controllers, and states that the model is a research simulator, not a validated clinical system.

    NVIDIA has released Cosmos3-H-Dreams, specialized from the 16-billion-parameter Cosmos3-H-Embodied-Nano for action-conditioned video generation of cholecystectomy. The model is trained with 28 action dimensions, 832 x 480 video, and 10 Hz action alignment, and distillation yields two student checkpoints, four-step and two-step; the two-step version is trained separately, offering faster inference but lower image quality. NVIDIA also provides the FlashDreams streaming runtime and methods for adap

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Tuesday

  1. The New StackSelected

    DeepSeek open-sources DeepInfer, the inference engine behind its API, with support for both Nvidia and Huawei Ascend

    DeepSeek has open-sourced DeepInfer, its in-house inference framework used to serve its models in production. Released under the Apache 2.0 license, it lets developers run DeepSeek-V4.1-Flash on their own infrastructure, with support for subsequent models planned. The framework includes optimized compute kernels and communication libraries such as FlashMLA, DeepGEMM, and DeepEP, supports speculative decoding, asynchronous scheduling, and structured output, and also open-sources the deepseek-recipe library (available in Rust and Python versions) for handling request and response transformations. On the hardware side, it supports Nvidia SM100 (Blackwell) architecture and the Huawei Ascend 950 NPU series from day one; older architectures such as Hopper are not listed. Batch-invariant inference and deterministic sampling are available only on Nvidia hardware, and the published performance data is also based on Nvidia GPUs. The current version supports offline inference but lacks complete production serving components such as KV cache management, cross-machine inference stage separation, and replica load balancing.

    put, and also open-sources the deepseek-recipe library (available in Rust and Python versions) for handling request and response transformations. On the hardware side, it supports Nvidia SM100 (Blackwell) architecture and the Huawei Ascend 950 NPU series from day one; older architectures such as Hopper are not listed. Batch-invariant inference and deterministic sampling are available only on Nvidia hardware, and the published performance data is also based on Nvidia GPUs. The current version sup

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  2. The Elec

    Altera Connects to Nvidia Holoscan Sensor Bridge with FPGAs, Targeting the Physical AI Market

    Altera has launched FPGAs for the Nvidia Holoscan Sensor Bridge (HSB), targeting physical AI markets such as robotics and autonomous driving. The architecture connects in the order of sensors, FPGA, Ethernet, and GPU; the FPGA aggregates multi-protocol sensor data and handles low-latency tasks such as localization, motor control, and image preprocessing, then writes directly into Nvidia GPU memory over Ethernet, bypassing the CPU to reduce latency. Altera offers four FPGA series—Cyclone, Agilex 3, Agilex 5, and Agilex 7—and is working with Terasic and Critical Link to develop a sensor board and SOM ecosystem.

    Altera has launched FPGAs for the Nvidia Holoscan Sensor Bridge (HSB), targeting physical AI markets such as robotics and autonomous driving. The architecture connects in the order of sensors, FPGA, Ethernet, and GPU; the FPGA aggregates multi-protocol sensor data and handles low-latency tasks such as localization, motor control, and image preprocessing, then writes directly into Nvidia GPU memory over Ethernet, bypassing the CPU to reduce latency. Altera offers four FPGA series—Cyclone, Agilex

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Monday

  1. Modal Official Engineering Blog

    NVIDIA Vera CPU Doubles Modal Sandboxes Throughput

    NVIDIA delivered the first Vera CPU server to Modal. On Modal's self-built Agentless Agent Bench (50 Terminal-Bench oracle tasks), Vera increased Modal Sandboxes throughput by 2x. Vera is NVIDIA's new Arm-compatible CPU for the Vera Rubin GPU platform, and can also be used as a standalone CPU server; Modal previously ran only x86 machines and needed to rework its infrastructure and image build system to support Arm. Modal says that under heavy load Vera's latency performance is actually better, breaking the usual throughput-latency tradeoff.

    Serial task latency in seconds for ten selected workloads on NVIDIA Vera, Intel GNR, AWS Graviton 4, AWS Graviton 5, and AMD Zen 5

    NVIDIA delivered the first Vera CPU server to Modal. On Modal's self-built Agentless Agent Bench (50 Terminal-Bench oracle tasks), Vera increased Modal Sandboxes throughput by 2x. Vera is NVIDIA's new Arm-compatible CPU for the Vera Rubin GPU platform, and can also be used as a standalone CPU server; Modal previously ran only x86 machines and needed to rework its infrastructure and image build system to support Arm. Modal says that under heavy load Vera's latency performance is actually better,

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  2. PyTorch Blog

    One Search, Two Layers of Optimization: PyTorch Helion Introduces Compiler Controls with NVIDIA CompileIQ

    PyTorch Helion can now search ptxas compiler control parameters through NVIDIA CompileIQ, jointly with the existing kernel configuration search, further tapping performance headroom on the roughly 27 kernels that have already been pre-tuned. CompileIQ packages tuning results as a portable Advanced Control File (ACF); Helion supports specifying a fixed ACF, and also supports searching multiple ACFs together as one dimension of autotuning. These compiler controls are exposed by ptxas starting from CUDA 13.3.

    Helion logo

    PyTorch Helion can now search ptxas compiler control parameters through NVIDIA CompileIQ, jointly with the existing kernel configuration search, further tapping performance headroom on the roughly 27 kernels that have already been pre-tuned. CompileIQ packages tuning results as a portable Advanced Control File (ACF); Helion supports specifying a fixed ACF, and also supports searching multiple ACFs together as one dimension of autotuning. These compiler controls are exposed by ptxas starting from

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