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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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ction set principle. It is open and royalty-free, unlike proprietary frameworks such as those from Intel Corp. and Arm Ltd. or, more importantly, CUDA, the standard that underpins Nvidia Corp.’s chipset. Nvidia currently dominates the physical AI market by a wide margin, acting as the gravitational center by providing the hardware and software stacks. Estimates for the global market itself vary in scale, but it is projected to surpass $430 billion by 2030 and approach $1.6 trillion by 2040, acco

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covering its core technology, with more pending, while its team brings over 250 career patents in computing, over 350 peer-reviewed publications and silicon experience from Intel, Nvidia, Apple and Texas Instruments. With unified memory and standard RISC-V tools, the Ubitium architecture provides a common processor and toolchain in place of multiple specialised processors. By reducing data transfers between chips and simplifying software development, Ubitium aims to make intelligent machines mor

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the AI companies to adhere to a slowdown if one of the group does not. Right now, Silicon Valley is split into two camps regarding AI safety risk. On the one hand, Zuckerberg and Nvidia CEO Jensen Huang promote a more accelerationist agenda, arguing that the risks have been overstated and that market forces will push AI companies to self-regulate. On the other hand are more risk-averse leaders, most notably Anthropic CEO Dario Amodei. In September, he published a blog post calling for “speed li

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25). Router wars: Which MoE routing strategy actually works. Cerebras Blog. https://www.cerebras.ai/blog/moe-guide-router Appendix Table 1: NCCL all-to-all bus bandwidth results on an 8×H100 SXM5 node. Results show bandwidth converging to 350 GB/s at larger message sizes. The nvcr.io/nvidia/pytorch:24.10-py3 docker container was used for the software environment, and the benchmark under test is nccl-tests all-to-all bandwidth. The MoE 101 Guide MoE Math Demystified: What Does 8x7B Actually Mean?

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o, at least in theory, the ones that pay for AI compute, which means that we also have a very, very poor understanding of the actual customers behind those hundreds of billions of NVIDIA GPUs. In fact, that’s a great place to go next. People are going to get mad at me for this because they are cowards, but I posit that we have little understanding of the true price that people are paying for AI GPUs, or about the underlying economics of AI data centers writ large. Yes, yes, I know, you’re going

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ard of robotics startups. We also cover world-action models versus vision-language-action models, GPT-6 and coding agents in robotics, and Jason’s journey from AlphaGo and Penn to NVIDIA, Google DeepMind, and founding Dyna. We discuss: Why Dyna-2 was trained on over one million hours of human video; World-action models: predicting future video to guide physical robot actions; Diffusion transformers, flow matching, and real-time robotic control; How robots learn new tasks with as little as 45 min

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ard of robotics startups. We also cover world-action models versus vision-language-action models, GPT-6 and coding agents in robotics, and Jason’s journey from AlphaGo and Penn to NVIDIA, Google DeepMind, and founding Dyna. We discuss: Why Dyna-2 was trained on over one million hours of human video World-action models: predicting future video to guide physical robot actions Diffusion transformers, flow matching, and real-time robotic control How robots learn new tasks with as little as 45 minute

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heCUBE Pod: AI doubters get loud, and IBM seeks competitive advantage by Devony Hof Americans have big concerns about artificial intelligence, but that hasn’t stopped OpenAI Inc., Nvidia Corp., IBM Corp. and others from charging ahead. Last week saw shares of Nvidia, CoreWeave Inc., Oracle Corp. and Nebius Group N.V. decline amid reports that OpenAI’s annualized net revenue was approximately $50 billion, roughly $20 billion below previously reported figures. Despite the ongoing public pushback a

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r what they reveal about the company than for the products themselves. As Mike Wheatley outlined in his SiliconANGLE post, Cisco rolled out a new pluggable optics form factor with Nvidia Corp., shipped its first Silicon One G300 systems, introduced the G302 top-of-rack chip and a new out-of-band management platform, and brought its Live Protect runtime security capability to SONiC. His post detailed the announcements, but it’s worth a deeper look at why these matter for artificial intelligence a

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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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crosoft to McKinsey are limiting usage for employees, as the flood of new tools and rapacious consumption weighs on bottom lines. Others, such as Palantir, Booz Allen Hamilton and Nvidia, are restricting employee use over growing concerns about the security of their organizations’ data with AI companies, Reuters reported. Microsoft and Walmart declined to comment. Meta, Palantir, Nvidia and Booz Allen Hamilton did not respond to requests for comment. “We initially treated AI like a traditional t

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show you how you can benefit from this work. We used a representative MoE model as our primary example. Swap-AB for WGMMA utilization To maximize tensor processing throughput, the NVIDIA Hopper architecture relies on the Warp Group Matrix Multiply Accumulate (WGMMA) instruction protocol, which executes collectively across a cooperative warpgroup of 128 threads. This operand asymmetry is fundamentally an instruction set architecture (ISA)-level property. In the underlying wgmma.mma async assembly

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r of Product Marketing at Cerebras, where he launched Cerebras Inference and the CS-3 AI accelerator. Before that James was lead AI analyst at ARK Invest. He started his career at NVIDIA, launching the first CUDA-capable GPUs in the early days of deep learning. Ikenna Izugbokwe Ikenna is a Principal Solutions Architect at AWS specializing in networking, containers, and AI infrastructure. He guides model providers through scaling their training and inference systems while enabling rapid deploymen

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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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acy and Cost per 1,000 Images: Moondream 3.1 and Frontier Models.” Unpublished internal evaluation, October 2026; Prime Intellect. “Prime Inference: Fast, Reliable Serving for Frontier Open Models.” Prime Intellect Blog, 2026; Moondream. “Photon Serving Benchmarks, NVIDIA H100.” Unpublished internal measurements, October 2026; Vigliarolo, Brandon. “Cloudflare Tries to Outplay Jev with Open-Weight Clef Models.” The Register, October 1, 2026 Rewriting Prime Agent in Rust On the Nature of the Swarm

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":null,"isHoverDragHandle":false},"content":[{"type":"text","text":"2026 年 10 月第一周，GitHub 官方 Trending 和 OpenGithubs 飙升榜同时验证了这条赛道的热度：12 个总榜项目中有 6 个与 Agent 直接相关，20 个飙星榜项目中有 12 个同属此列。NVIDIA/OpenShell、google/ax、TencentCloud/Octop 分别代表芯片厂、搜索厂、云厂商在 Agent 运行时上的下注。"}]},{"type":"image","attrs":{"id":"fee3b4e9-cf16-45e7-b925-c94245f67503","src":"https://developer.qcloudimg.com/http-save/audit-1051367/a90444a718832982d02109af6d7c19d8.png","extension":"","align":"center","alt":"","showAlt":false,"href":"","

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gen in-house AI inference chip, rolling out to production in 2027. OPENAI 🔥: - Codex got another global usage-limit reset on Day 8. - OpenAI reopened applications for its Codex Ambassador program. NVIDIA 🔥: - NVIDIA is opening its Vera Rubin architecture to the Open Compute Project ecosystem to help scale AI factories. AMAZON 🔥: - Amazon Bedrock AgentCore now has consumption-based pricing for short-term memory. Used Grok to compose this brief, cherry-picking the news and doing some post-editing.

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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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ficially announced that Microsoft will make its in-house Maia chips the primary inference system for its products, scaling to multiple gigawatts. This will make it less reliant on NVIDIA hardware, and Maia 300 is meant to replace AMD chips too, not only Nvidia. The key claim is 40% more tokens per megawatt than leading GPU-based systems A full rack packs 96 accelerators into 167kW with 27.6TB of HBM, and Microsoft claims it delivers up to 7x the token throughput of a Maia 200 rack. The shift wil

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