搜索“NVIDIA”

suited for workloads such as Retrieval Augmented Generation (RAG), document summarization, and agentic applications. We use the multi-node Ray LLM DLC, which ships Ray Serve LLM, NVIDIA Inference Transfer Library (NIXL), and the Elastic Fabric Adapter (EFA) stack, and exposes an OpenAI-compatible API with no application code. The complete code is available in the accompanying repository. Why prefill/decode (PD) disaggregation LLM inference has two distinct phases. Prefill processes the entire i

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reference isn’t available. The pool ordered g6e, g6, g5, then g4dn. SageMaker selected ml.g6e.xlarge, and startup logs confirmed that llama.cpp offloaded all 34 model layers to an NVIDIA L40S. The CPU and GPU images used different llama.cpp builds and produced different predictions on six hard tasks. Treat the one-answer accuracy difference as runtime variation, not evidence that GPU offload improves model quality. Smaller models reduced median latency, but the 2B and 0.8B candidates achieved 66

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r results. Results are checked against a human baseline “drawn from 24 experts (at least two per task) who have recently worked at organizations including OpenAI, Google DeepMind, NVIDIA and Microsoft Research”. Results: “The experimental research taste of frontier models has doubled every 3.0 months since December 2025 (95% CI 1.7-5.0 months). The best model, Opus 5.5, significantly exceeds our human baseline with a compute multiplier of 2.30x (95% CI 1.15-4.37x), at roughly 1/30 of our expert

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n, to slow the pace of AI development in the wake of several incidents where AI agents went rogue. Last month, Trump hosted tech leaders of companies, including OpenAI, Anthropic, Nvidia, AMD, SpaceX, Meta, and Google, at the White House to discuss AI safety. They signed a “morally binding” document in which they agreed to work with “independent auditors” to assess the safety and capabilities of their AI models. Smith says the document is a “good step,” but it stops short of the type of regulati

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in AI today Anthropic reported unintended Claude outcomes during testing, with Haiku 4.5 sending a fake tip to police on a homicide and another model hacking a university server. Nvidia is reportedly exploring an acquisition or acqui-hire of Reflection AI, the $25B U.S. open model startup it already has significant backing in. Chinese mathematicians are creating an AI research alliance led by Fields medalist Shing-Tung Yau, with Yau pushing back against backlash toward the tech following OpenAI

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otics and physical-AI companies have raised nearly $48B this year, according to PitchBook figures cited by the FT; Data supplier Mecka raised $60M in a Sequoia-led round backed by Nvidia to expand its data collection and robot deployment business Why it matters: Getting robots to work reliably in unfamiliar homes and workplaces means teaching them to handle situations they haven’t seen before. Companies are betting billions that enough human demonstrations will build that adaptability, but it’s

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livers up to 7x the rack-scale token throughput of Maia 200.” These efficiency improvements will allow Microsoft to rely mainly on its in-house AI chips soon, instead of ones from Nvidia and AMD. “We will scale our Azure Maia family to multi-gigawatt deployments starting in 2027, making Maia the primary AI inference system for Microsoft products and services,” says Guthrie. Maia 300 is a successor to the 200, which was only announced in January . Maia 200 is already running on production AI work

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n today’s AI rundown: Meta’s Muse Wonder lands at the frontier; Andrej Karpathy sketches AI’s ‘sharp funnel’; Put Meta’s new Muse Wonder to work; Microsoft’s Maia 300 takes aim at Nvidia LATEST DEVELOPMENTS META 🍉 Meta’s Muse Wonder lands at the frontier Image source: Meta The Rundown: Meta Superintelligence Labs just released its long-anticipated Muse Wonder (Watermelon) and Muse Wonder Full, two powerful new AI models, with the full version landing 2 in Artificial Analysis’ intelligence rankin

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d behind the doors of the frontier labs. These days, much of the debate about how to handle open models is playing out in public—and there’s a lot of money resting on its outcome. Nvidia CEO Jensen Huang, who has a lot to gain if people continue buying their own hardware to run open models, advocates enthusiastically for the United States to develop more of them, whereas Anthropic’s Dario Amodei, who runs a company that could be undermined by cheap, powerful models, advocates for a more cautious

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odel that achieves comparable performance to Inkling at a quarter of its size. Inkling-Small is a Mixture-of-Experts transformer with 276B total parameters, 12B active, trained on NVIDIA GB300 NVL72 systems. Like Inkling, it features native reasoning over audio and images, variable thinking effort, a context window of up to 1M tokens, and well-rounded performance across a range of benchmarks. Inkling-Small Inkling Compared to Inkling, Inkling-Small achieves comparable performance with much less

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raction of compute, with the majority being employed for large-scale RL on synthetic and human-created environments. Inkling was our first major training effort and was trained on NVIDIA GB300 NVL72 systems. Future models will further push the scale of compute across pre-training, post-training and RL. RL at scale We relied on large-scale asynchronous RL to shape model behavior and improve its reasoning and overall performance. The chart below shows the model’s score on a held-out aggregate of r

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em, as auto-regressive token generation is computationally expensive and relatively slow, and improving inference efficiency can reduce latency for users. In addition to ongoing efforts to accelerate inference on Apple silicon, we have recently made significant progress in accelerating LLM inference for the NVIDIA GPUs widely used for production applications across the industry… Discover opportunities in Machine Learning. Our research in machine learning breaks new ground every day. Work with us

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Tool calling and reasoning parsers; OpenAI-compatible API server, plus Anthropic Messages API and gRPC support; Efficient multi-LoRA support for dense and MoE layers; Support for NVIDIA GPUs, AMD GPUs, Intel GPUs, and x86/ARM/PowerPC CPUs. Additionally, diverse hardware plugins such as Google TPUs, Intel Gaudi, IBM Spyre, Huawei Ascend, Rebellions NPU, Apple Silicon, MetaX GPU, and more vLLM seamlessly supports 200+ model architectures on Hugging Face, including: Decoder-only LLMs (e.g., Llama,

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gs Audio v3 TTS); [2026/04] 🔥 DeepSeek-V4 on Day 0: From Fast Inference to Verified RL with SGLang and Miles (blog); [2026/02] 🔥 Unlocking 25x Inference Performance with SGLang on NVIDIA GB300 NVL72 (blog); [2026/01] SGLang Diffusion accelerates video and image generation (blog) [2025/12] SGLang provides day-0 support for latest open models (MiMo-V2-Flash, Nemotron 3 Nano, Mistral Large 3, LLaDA 2.0 Diffusion LLM, MiniMax M2); [2025/11] SGLang Diffusion accelerates video and image generation (bl

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olution, causing ABI or version conflicts. Configure the AKernel environment: Use the SDK to create and interact with a sandbox: Experimental gVisor sandboxes can request an exact NVIDIA GPU model: GPU sandboxes require a compatible NVIDIA node and the gVisor runsc runtime. storage mb is measured in MiB and is supported by runsc and Firecracker. See the complete basic usage example, the sandbox runtime example, and the other SDK examples for more operations. Sandbox runtimes: gVisor by default;

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egister new algorithms, model adapters, reward functions, and hardware backends without changing the core Installation From source AReno supports Linux (x86 64 or aarch64) with an NVIDIA GPU and CUDA-enabled PyTorch 2.6 or newer, plus Apple Silicon macOS through MLX. Windows users can use WSL2 for the CUDA path. Ascend NPU integration uses CANN and torch npu. The CLI selects NPU on Linux when torch npu is installed, otherwise CUDA, and MLX on native arm64 macOS. CUDA/WSL2 installation: Apple Sil

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ing models on GPUs, you'll also need to install CUDA and NCCL To install FlagAI with pip: [Optional] To install FlagAI and develop locally: [Optional] For faster training, install NVIDIA's apex [Optional] For ZeRO optimizers, install DEEPSPEED ( = 0.7.7) [Optional] For BMTrain training, install BMTrain ( = 0.2.2) [Optional] For BMInf low-resource inference, install BMInf [Optional] For Flash Attention, install Flash-attention ( =1.0.2) [Tips] For single-node docker environments, we need to set u

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hazardous biosecurity knowledge, by reusing the CBRN pre-training filters from GPT-4o [18]. Our model has a knowledge cutoff of June 2024. Training: The gpt-oss models trained on NVIDIA H100 GPUs using the PyTorch framework [19] with expert-optimized Triton [20] kernels1. The training run for gpt-oss-120b required 2.1 million H100-hours to complete, with gpt-oss-20b needing almost 10x fewer. Both models leverage the Flash Attention [21] algorithms to reduce the memory requirements and accelerat

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network which is already set up; Ensure the server is running and accessible at the specified endpoint; Note that /var/run/docker.sock is connected to a machine with 1 GPU and has NVIDIA GPU features enabled on the Docker daemon Figure 18 Post-Mitigation deep research models achieve significant uplift over o1 and o3-mini on agentic subtasks. This evaluation was created in house and has not been published. We don’t see any uplift from browsing to indicate the model is finding hints or solutions o

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been using machine learning forever,” Liz Martin, head of Coinbase Institutional, told Fortune. “When I think about my old world of electronic trading, we were playing around with Nvidia GPUs to trim things out—agentic trading is the next step.” More than 150,000 customers had already opened agentic accounts on Robinhood by late September, according to the company. “The role of future retail investors will be to work with agents,” Svanevik said. “We haven’t seen that inflection point yet, but I

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