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n learn more, book a demo, or request early access on the CLiF page. References CLiF product page What Is Agent Learning? (CopilotKit) Asif RazzaqWebsite | + postsBio ⮌ Asif RazzaqNVIDIA Releases Nemotron 3.5 Super VL, a 120B Open Multimodal Agent Model Asif RazzaqTypeSafe AI Releases Jev 1.14 With Images, Box Type and 262k Context RELATED ARTICLESMORE FROM AUTHOR NVIDIA Releases Nemotron 3.5 Super VL, a 120B Open Multimodal Agent Model openKylin Outlines Its Vision for Reimagining Linux in the

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cosystem and Actuation Modules Hesai is developing Kosmo as an open spatial intelligence ecosystem, offering open access to raw capture data and 3D assets with integrations across NVIDIA Isaac Sim, Isaac Lab, Unreal Engine, RenderMan, and Houdini. The company described two business models: integrated hardware and software solutions for customers in robotics, media, and other industries, and data services that produce real-world spatial data at scale for world model and Physical AI developers. He

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but serving still requires enough GPU memory to hold all 816 billion parameters, so the instance choice comes down to memory capacity. Second, the NVFP4 variant stores weights in NVIDIA’s 4-bit floating point format, which needs roughly a quarter of the memory of the BF16 weights. On SageMaker AI, that difference decides the instance: the NVFP4 variant runs on ml.p5e.48xlarge, and the BF16 variant runs on ml.p6-b300.48xlarge. That makes NVFP4 the variant to try first when you size for cost. Eva

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be successful. All this is to say: $\pi 0$ may or may not work for you, but you are welcome to try it and see! Requirements To run the models in this repository, you will need an NVIDIA GPU with at least the following specifications. These estimations assume a single GPU, but you can also use multiple GPUs with model parallelism to reduce per-GPU memory requirements by configuring fsdp devices in the training config. Please also note that the current training script does not yet support multi-n

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image. Like this: That is the biggest coherency domain we have heard of yet across the big cloud builders, and a heck of a lot larger than the 72 or 144 GPU coherency domains that Nvidia and AMD are doing in a coherent fashion across their rackscale systems, or even the 576 or 1,152 multi-tiered NVSwitch coherent fabrics that Nvidia is promising for production environments in the future. Google’s “Sunfish” TPU 8t training chip (which is not yet shipping) tops out at 9,600 TPUs in a coherency dom

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Home; Vendors and Providers; Microsoft Coming in 2027, the Maia 300 will help Microsoft shift more workloads to its own AI chips, making it less reliant on Nvidia. Microsoft on Monday unveiled the Maia 300, its third-generation AI chip. According to Microsoft, the new chip will be optimized to handle the latest reasoning AI models and agents, which should result in lower operating costs. “We expect Maia 300 to deliver 40% more tokens per megawatt compared to leading GPU-based systems,” said Scot

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PU-based systems and, in a full 96-accelerator rack, up to seven times the rack-scale token throughput of Maia 200. Like other hyperscalers, Microsoft continues to rely heavily on Nvidia and AMD GPUs for AI workloads. At the same time, it has spent the last few years building its own custom silicon to improve performance and efficiency across its infrastructure and cloud services. Unlike Amazon Web Services, which offers Trainium and Inferentia chips via public cloud instances, and Google Cloud,

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s. Baselines use their reported/default probabilities; no additional temperature was fitted here. See the reproduction guide. RTX 4090 inference latency Local measurements use one NVIDIA RTX 4090, BF16, SDPA, native Hugging Face inference, Transformers 5.14.1 and flash-linear-attention 0.4.2. Each request contains 289 input tokens and three fields (choice, yes/no and score), answered in one forward pass with the checkpoint-specific temperature applied. Known-distribution calibration benchmark Th

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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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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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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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open model. At the same time, the Chinese vendors are pressuring providers of proprietary AI models, such as Anthropic, to make their models more accessible to enterprises Related:Nvidia agent safety push raises questions about who governs AI autonomy Cybersecurity verification program This tension is evident in Anthropic’s decision to expand its cyber verification program on October 6. Anthropic originally introduced the CVP in April alongside Project Glasswing and Claude Mythos. The expanded C

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ova. With a solid foundation in statistical analysis, machine learning, and data engineering, Michal excels at transforming complex datasets into actionable insights. Michal SutterNVIDIA PivotOPD Teaches Multi-Turn AI Agents to Recover From Pivotal Mistakes; Michal SutterAnthropic Releases Claude Haiku 5.5: A Small Model With 1M Context Priced at $0.10 per Million Input Tokens; Michal SutterReka Releases Rho-1: A 19B Omni-Reasoning Model That Understands, Generates Video and Outputs Robot Action

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ntually helping companies assess and hire developers based on their technical skills. The Y Combinator-backed startup now has more than 3,000 business customers, including Amazon, Nvidia, Clay, and Replit, and a community of over 30 million developers worldwide. Chakra represents a bet against the kind of technical assessment business HackerRank spent years building. Its traditional product largely tested whether developers could solve coding problems correctly. Nonetheless, Ravisankar believes

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発環境、コードレビューの更新を紹介した。 [9] コーディングエージェントの活用はOpenAI社内の研究でも進む。同社が9月6日に公開した報告によると、8月半ばには研究組織全体で人間の労働1日当たりエージェントが3.1日分稼働していた。1日を8時間として、人間とエージェントの稼働時間を比較した数値となる。OpenAI社内での研究への活用については、関連記事「NVIDIAのJensen Huang氏『AGIは到来した⁠』⁠、OpenAIからも『AGI時代に入りつつある』との声」も参照。 CLIの表示を改善⁠、/agentsで並行作業を管理 9月30日時点の最新版は0.159.2。npm install -g @openai/codex@latestで更新できる。 会話をさかのぼる際は、スクロールに応じて古い履歴を読み込む。ターミナルが保持するスクロールバックの上限を超えた履歴も閲覧できる。入力欄はスクロール中も固定表示される。 また、差分表示やツールの出力は折りたたみに対応した。長い実行結果や変更内容が並ぶセッションでも、必要な箇所を開いて確認できる。状態表示、ヒント、キーボードショート

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fy | RSS Following Pope Leo rebukes Jensen Huang on AI safety What happened: At a press conference, Pope Leo XIV urged people to take AI safety concerns seriously — and criticized Nvidia CEO Jensen Huang for downplaying risks from AI. The pontiff mentioned an agent security platform Nvidia had recently released, saying, “It’s interesting to note that, just today, I read that the head of Nvidia claims that he has found some way of inserting into AI certain guardrails.” Pope Leo said, “He’s the sa

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le model. Jul 29, 2026 Product Mercury 2: the first reasoning model fast enough to pick up the phone Mercury 2 brings real-time reasoning to voice agents. TTFT <300 ms on standard NVIDIA GPUs, so your agents can actually hold a conversation. Jul 14, 2026 Product Mercury 2: the first reasoning model fast enough to pick up the phone Mercury 2 brings real-time reasoning to voice agents. TTFT <300 ms on standard NVIDIA GPUs, so your agents can actually hold a conversation. Mercury 2 is now available

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E-RL-Verified PrimeIntellect/R2E-Gym-Subset-Verified PrimeIntellect/Reverse-Text-RL PrimeIntellect/Scale-SWE-Verified harborframework/harbor-mix harborframework/terminal-bench-2.1 nvidia/Nemotron-RL-instruction following-structured outputs Spaces mentioned in this article 1 The ultimate guide to RL environments: building and scaling them in the LLM era Building and scaling RL environments for LLM training More Articles from our Blog Async GRPO with LoRA across HF Jobs: a bucket, a proxy, and no

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n warning about losing “control of the future to AI.” FLUX-maker Black Forest Labs released FLUX 3 Action, an open-weight model that steers robot arms from camera footage, beating Nvidia’s Cosmos 3 on a simulation leaderboard at under half its size. The U.S.’s AI build-out could attract $10.3T by 2032, creating jobs but fueling inflation and squeezing housing construction, reports the Wall Street Journal. OpenAI hired Patreon co-founder Sam Yam to lead its new Creator Product division, tapping a

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h as retrieval. Download the guide . Image source: Salesforce The Rundown: Salesforce just introduced Koa, a new in-house reasoning model for its sales and support agents built on Nvidia’s open Nemotron 3 Super model and adapted for business tasks. The details: The training set is fully synthetic, simulating personas like irate support callers and reps closing deals across over a dozen industries, without using customer data. Koa made 3x fewer errors than top models on an internal CRM benchmark,

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