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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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s 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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Five-year commitment targets AI and scientific research capabilities JEONG IL JOO; Published 2026.10.13 14:28; Reply 0 Nvidia announced Oct. 12 that it will invest $1 billion over the next five years to build superintelligence capabilities and strengthen scientific research in the United States. Nvidia Founder and Chief Executive Officer (CEO) Jensen Huang said at the "Science: The New Golden Age" event in Washington, D.C., on Oct. 8 that the investment would give U.S. scientists access to advan

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y Data Processing Units (DPUs), graphics processing unit (GPU) servers, storage servers, and networking technology help cut infrastructure costs and boost operational efficiency. "Nvidia and MangoBoost are the only companies with this kind of business model," Kim added. MangoBoost is also expanding its data center infrastructure to support Mango Inference. It plans to establish a 1-megawatt (MW) AI data center in Gangnam District, Seoul, by the first quarter of next year, with plans to expand ca

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the kernel already tracks usage per container for you, so getting a per-pod number is basically free. GPUs on the other hand, don’t give you that. The tool everybody starts with, NVIDIA’s DCGM exporter, reports at the physical device level by default. This is fine when one pod has a GPU to itself, but the second you start sharing a GPU across pods, with time-slicing or MPS, DCGM gives every pod sharing that GPU the same value. So if three pods are sharing one GPU, all three look like they’re us

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atistical analysis, machine learning, and data engineering, Michal excels at transforming complex datasets into actionable insights. Michal SutterWhat is Decision 3.0? vLLM Semantic Router’s New Open Decision Models; Michal SutterMeet the Underdog Saluki 27B: A 2-bit Qwen3.8-27B That Beats the Original at Tool Calling; Michal SutterArchitect Launches Liquid Inference, a Real-Time Auction for LLM Inference; Michal SutterNVIDIA PivotOPD Teaches Multi-Turn AI Agents to Recover From Pivotal Mistakes

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gentic Infra） 战略布局，贯通算力资源、Token生产与行业应用三层价值链。 其中，“一中心”是算力集散中心，即Agentic Infra自主式基础设施平台。目前该平台已部署算力达37000P，覆盖16种主流芯片。无问芯穹此前发布了全球首个千卡规模异构芯片混训平台，具备万卡扩展性，已实现华为昇腾、天数智芯、沐曦、摩尔线程四种国产芯片分别与AMD、NVIDIA两种进口芯片进行大模型混合训练，算力利用率最高达到97.6%。 “后厂”是Token工厂，对应智能体模型即服务 （Agentic MaaS） 大模型服务平台。今年7月，无问芯穹在世界人工智能大会上发布了新一代推理系统优化成果——跨集群异构PD分离架构 （PDD） ，基于Agentic MaaS线上业务长期大规模的实践积淀，该架构在实现了首Token延迟 （TTFT） 降低51.5%的同时，单Token成本可降低37.5%。过去一年里，无问芯穹的Token工厂已通过一系列原始技术创新，推动推理成本降至原来的十分之一，截至7月，无问芯穹Agentic MaaS平台单日Token调用量较去年12月实现了40倍增长。 “前店”是

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产芯片不断发展，算力供给逐渐呈现多元化，新的问题随之出现：不同品牌、不同架构的芯片如何协同工作？如何将分散的算力资源转化为稳定、高效、可持续使用的模型训练与推理能力？ 这正是无问芯穹团队长期深耕的方向。公开资料显示，无问芯穹在2024年便完成了全球首次“4+2”式700亿参数大模型异构芯片混合训练，运用华为昇腾、天数智芯、沐曦、摩尔线程四种国产芯片与AMD、NVIDIA两种进口芯片，算力利用率达97.6%。 目前其已可以实现2个工作日完成新芯片适配，国产芯片适配成本降低70%以上；在万亿参数级MoE模型训练中，其吞吐较主流开源框架提升41%至75%，在推理上更是实现成本一年内下降10倍。 这些指标的背后，是团队从实验室到产业一线，始终深耕多元异构与软硬件协同创新的技术积累。 从长期看，国产化与算力供给多元化、碎片化，将使异构协同成为持续性的产业需求。无问芯穹提前围绕“M种模型×N种芯片”构建技术体系，恰好覆盖了这一长期需求。 这种先发优势，让它在国产异构布局中占据了有利位置。 二、前店后厂一中心：不碰模型不碰芯片，中立平台卡位Token需求爆发 随着Agent应用加速发展，AI基础设施

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calls for global rules on AI, and says the UK is in the "best position" to lead the charge. At the end of last month, Trump hosted the leaders of US tech giants OpenAI, Anthropic, Nvidia, SpaceX, Meta, and Google at the White House where the executives signed a "morally binding" document. The companies agreed to work with "independent auditors" to assess whether their AI systems are working as intended and make sure they do not "hack or access technical systems in unintended ways". Smith said he

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and data engineering, Michal excels at transforming complex datasets into actionable insights. Michal SutterMeet the Underdog Saluki 27B: A 2-bit Qwen3.8-27B That Beats the Original at Tool Calling; Michal SutterArchitect Launches Liquid Inference, a Real-Time Auction for LLM Inference; 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

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