搜索“NVIDIA”

26/02] Support Qwen3.5; [2026/02] Support vllm-project/llm-compressor 4bit symmetric/asymmetric quantization. Refer here for a detailed guide [2025/09] TurboMind supports MXFP4 on NVIDIA GPUs starting from V100, achieving 1.5x the performance of vLLM on H800 for openai gpt-oss models!; [2025/06] Comprehensive inference optimization for FP8 MoE Models; [2025/06] DeepSeek PD Disaggregation deployment is now supported through integration with DLSlime and Mooncake. Huge thanks to both teams!; [2025/

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man consumption, and into action predictions for embodied agents. A model that masters only rendering, or only planning, cannot do either. The commercial surface area is enormous. NVIDIA’s Omniverse alone targets what the company estimates as more than a trillion dollars of addressable market in factories, warehouses, supply chains, and digital twins. Robotics training, autonomous vehicle testing, architectural visualization, engineering, and drug discovery all depend on something simulation-sha

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. See vLLM Usage part; [2026.02.25] 🔥 We release FireRedASR2-LLM model weights. 🤗 🤖; [2026.02.13] 🚀 Support TensorRT-LLM inference acceleration for FireRedASR2-AED (contributed by NVIDIA). Benchmark on AISHELL-1 test set shows 12.7x speedup over PyTorch baseline (single H20); [2026.02.12] 🔥 We release FireRedASR2S (FireRedASR2-AED, FireRedVAD, FireRedLID, and FireRedPunc) with model weights and inference code. Download links below. Technical report and finetuning code coming soon Available Model

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模态）、EmbeddingGemma 300M。实测性能：Gemma-3n E2B在4K上下文下预填充1600+ tokens/秒，解码28 tokens/秒，LLM工作负载吞吐量为CPU的12倍、GPU的10倍。 全场景产品路线图 合作生态 Meta Llama 2/3/3.2 Google LiteRT/Gemini Nano OPPO Omni多模态 NVIDIA GB10 Grace Blackwell 联发科与NVIDIA联合设计GB10 Grace Blackwell Superchip，搭载于NVIDIA DGX Spark平台。在MWC 2026上展示了与OPPO合作的Omni多模态大模型端侧集成。同时自研UCIe-Advanced IP（芯粒间互联），全球首个在台积电2nm和3nm工艺上硅验证。 Intel 端侧AI战略与规划 NPU代际演进：从10 TOPS到50 TOPS 英特尔AI Boost NPU经历了四代主要演进，NPU算力实现了跨越式增长。从Meteor Lake到Lunar Lake，NPU算力从约10 TOPS跃升至48 TOPS，实现了约4.8倍的

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a、LM Studio一键安装）。最佳本地编程模型包括Qwen 3 Coder、Devstral 2、DeepSeek V4、Gemma 4。小型编程模型（4B-14B参数）在HumanEval达75%至87%，与云端85%至92%差距缩小。本地模型硬件投资后每次查询成本为0美元。 企业级本地部署：对受监管行业，本地部署正从"锦上添花"变为"采购必备条件"。NVIDIA推出NeMo Guardrails自托管方案，Tabnine提供气密部署，卓易信息EazyDevelop支持私有化部署与全链路审计合规。 5.1 "四分天下"：中国AI编程IDE市场 截至2026年5月，国内AI IDE形成四大产品竞逐：腾讯CodeBuddy（28%）、阿里通义灵码（26%）、字节Trae（24%）、百度文心快码（22%）。 腾讯CodeBuddy：国内唯一支持IDE插件、独立IDE、CLI三形态的工具，推出WorkBuddy企业智能体，PC端月访问量国内第一。腾讯走"自研+投资"双线：自研服务国内，参投Lovable获取海外入口。 字节Trae：基于豆包大模型，完全免费策略，分国内版trae.cn和国

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场关注的重点之一是Snap正在推进的SPECS AR眼镜及SPECS Intelligence业务。此前，Snap公布售价2195美元的SPECS消费级AR眼镜，并推出支持iPhone、Mac和SPECS跨设备使用的主动式AI助手SPECS Intelligence，进一步拓展空间计算与个人AI服务。 企业市场方面，Snap已与Salesforce、AWS及NVIDIA等公司建立合作，将SPECS接入Salesforce Agentforce、AWS相关AI服务及NVIDIA XR AI技术体系，探索现场服务、远程技术支持及零售等应用场景。 值得关注的是，Snap股东Blue Duck Capital于10月6日向公司董事会发出公开信，建议为SPECS业务引入独立外部融资，并提出约50亿美元的业务估值。 按照该机构的设想，若出售20%的SPECS业务股权，理论上可融资约10亿美元，用于支持后续研发及业务拓展。 Blue Duck Capital估计，SPECS每年需要约5亿美元投入，独立融资有望减轻相关支出对Snap核心广告业务的财务压力。该机构还提出，通过将广告业务与AR业务分别估值

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oduct to target the AI inference market with a heavily cost and power-optimized part. The resulting chip is not meant to be on the same performance level as flagship chips such as NVIDIA’s Blackwell Ultra or AMD’s MI455X, but then it will be a lot smaller and cheaper as well. Most recently, Intel delivered an update on Crescent Island back in August, where the company offered a fairly detailed look at the architecture behind the chip. Based on an enhanced version of their Xe3 GPU architecture th

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实验室，在 23.5 平方公里的真实场景中规模化应用，走进了党媒的国家级纪录片。从原始数据到数字资产，再到 AI 训练语料，一条真实空间的数据价值链，也在这里完成了扎实的探索。 但世界模型与空间智能成为中美科技竞争的新焦点，真实世界的三维数据，正在成为人工智能理解物理世界的关键入口。其域创新以自主创新筑牢根基，也以开放姿态走向世界：开放的 LCC 格式已被 NVIDIA Isaac Sim、Sketchup、Revit和3DS Max、超图等几十家国内外平台兼容。中国企业定义的数据格式进入全球主流技术生态，这是统筹“自主和开放”的一个生动注脚。 写实度高、生产效率快、落地成本低，3D 高斯扫描生成的三维数字资产，正走向更广阔的天地：文物古建的数字化保护，沉浸式云游与数字文创，在文化数字化的进程中，真实空间正在成为新的内容载体，党媒的文化传播，也由此多了一种可进入、可感知的表达。 《意见》明确提出强化企业科技创新主体地位、支持专精特新中小企业发展。作为国家级专精特新“重点小巨人”企业，其域创新坚持自主科技创新，产品入选《深圳市首台（套）重大技术装备推广应用指导目录》，构建了从空间采集、3

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chain’s generations), by model; medians ≈ 19.7k / 12.3k / 11.0k. Data from the inference setup eval. D. Edge Accelerators We can compute a better upper bound by using the pool of Nvidia Jetsons, which are edge accelerators. Unlike the traditional IoT botnet, IoT edge devices can serve a full model wholesale. A Jetson AGX Orin 64 GB runs Qwen3-30B-A3B at 76.7 tokens per second, and an AGX Thor reaches 226 tok/s. Note that we don’t believe they’re unusually vulnerable - this is just to count the

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ts with emptied context helps them escape “idea basins.” At equal compute it beats single runs and best-of-N on 12 of 13 long-horizon tasks (@DimitrisPapail); Other results:LoGRA (NVIDIA): Full-weight RL that uses less memory than LoRA, 7.2 GiB against 13.2 GiB at 1.5B (@omarsar0); nanoswe: A 1.7B coding agent trained from scratch for about $1,000 of compute scores 25.5% on SWE-bench Verified (@rdolmedo ); Test-writing gap: Frontier agents’ tests catch fewer than 55% of logical mutants (@nielstr

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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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6. The potential acquisition would give Amazon access to Decart’s chip-compatibility technology as the cloud computing giant expands its own AI hardware business and competes with NVIDIA. Amazon Web Services gave Decart early access to its Trainium3 AI chip in 2025 after meeting the startup’s founders, WSJ previously reported. Amazon has developed its Trainium processors as an alternative to NVIDIA’s AI chips. Beyond chip software, Decart is developing world models called Lucy and Oasis, which c

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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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