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er hosts powered by Arm-based CPU chips. In the AI accelerated segment of the market in Q1 2026, X86 AI hosts took in $34.6 billion in revenues, but thanks to the wide adoption of Nvidia’s “Grace” CG100 server processors in its rackscale NVL72 platforms using its “Hopper” and “Blackwell” GPUs, Arm-based system revenues surpassed X86-based system revenues in the AI segment, brining in Now, you have to remember that is the value of a server node or a rackscale system with all of its GPUs installed

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ks It won't kill you, or humanity, to get off the AI upgrade treadmill New models arrive so fast, we already have more new tech than it's possible to understand in a career Cisco, Nvidia team up to build fatter, faster pluggable If you thought 1.6 Tbps was fast, just wait, Cisco's USFP targets 12.8 Tbps in a single optical pluggable TOP STORIES Anthropic asks users to stop being mean to Claude; '123456' password linked to Denmark data breach affecting 8.8M people; Microsoft and Anthropic play in

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ks It won't kill you, or humanity, to get off the AI upgrade treadmill New models arrive so fast, we already have more new tech than it's possible to understand in a career Cisco, Nvidia team up to build fatter, faster pluggable If you thought 1.6 Tbps was fast, just wait, Cisco's USFP targets 12.8 Tbps in a single optical pluggable TOP STORIES Anthropic asks users to stop being mean to Claude; '123456' password linked to Denmark data breach affecting 8.8M people; Microsoft and Anthropic play in

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s it especially attractive for mid-sized models (7B–70B parameters on M series chips). Yet beneath this momentum lies a significant gap: many performance-critical kernels that the NVIDIA ecosystem takes for granted: paged attention, optimized SSM scan kernels, fused MoE routing are either absent or naive without hardware-specific tuning. MLX runs models correctly but often leaves significant performance on the table. This gap is what motivates the rest of this post. K-Search is an evolutionary k

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MD not only has roadmaps that are absolutely trustworthy in their own rights but looks good by comparison to Intel. AMD is without question competitive with AI industry juggernaut Nvidia on the hardware front, from chips to rackscale systems, and is getting it done on the software front with the help of hyperscalers and cloud builders who desperately want an alternative – or two, if you count their own XPU and CPU chippery – to the Nvidia stack to keep the heat on Big Green as well as to mitigat

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T4 weights, while rollout uses W4A16. Although memory efficient, the compute path still effectively uses BF16 activations with dequantized INT4 weights. The table below normalizes NVIDIA's HGX platform dense Tensor Core specs to per-GPU throughput: B200 and B300 from 8-GPU HGX systems, Rubin from the HGX Rubin NVL8 table. For RL systems, the precision contract spans: SGLang rollout; Megatron and TransformerEngine training; Hugging Face checkpoint conversion; Megatron-to-Hugging Face live weight

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ks It won't kill you, or humanity, to get off the AI upgrade treadmill New models arrive so fast, we already have more new tech than it's possible to understand in a career Cisco, Nvidia team up to build fatter, faster pluggable If you thought 1.6 Tbps was fast, just wait, Cisco's USFP targets 12.8 Tbps in a single optical pluggable TOP STORIES Anthropic asks users to stop being mean to Claude; '123456' password linked to Denmark data breach affecting 8.8M people; Microsoft and Anthropic play in

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ks It won't kill you, or humanity, to get off the AI upgrade treadmill New models arrive so fast, we already have more new tech than it's possible to understand in a career Cisco, Nvidia team up to build fatter, faster pluggable If you thought 1.6 Tbps was fast, just wait, Cisco's USFP targets 12.8 Tbps in a single optical pluggable TOP STORIES Anthropic asks users to stop being mean to Claude; '123456' password linked to Denmark data breach affecting 8.8M people; Microsoft and Anthropic play in

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renaissance for agentic AI will be enough to get Intel back to where it was a decade ago. But it still does not have an AI inference accelerator that can compete with the likes of Nvidia’s GPUs and its Grok LPUs for low latency inference. The partnership with SambaNova is important, but Intel does not control that technology and it likes control. Tan is chairman of SambaNova as well as chief executive officer at Intel, and the wonder is why they didn’t come to some kind of terms to merge. But on

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ks It won't kill you, or humanity, to get off the AI upgrade treadmill New models arrive so fast, we already have more new tech than it's possible to understand in a career Cisco, Nvidia team up to build fatter, faster pluggable If you thought 1.6 Tbps was fast, just wait, Cisco's USFP targets 12.8 Tbps in a single optical pluggable TOP STORIES Anthropic asks users to stop being mean to Claude; '123456' password linked to Denmark data breach affecting 8.8M people; Microsoft and Anthropic play in

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ickly enough to control a physical machine. BFL says the FLUX-mimic backbone can be optimized to run from input to internal representation in less than 80 milliseconds on a single NVIDIA RTX 5090. Further optimization across the action decoder, the inter-process path between sensors, model, and actuators, and real-time chunking that overlaps prediction with execution brings the full system’s reaction time to 101 milliseconds. Audi Production Lab has been testing and deploying the system on real

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-engineering an exploit the same as launching one. Guardrails on Opus tripped every time we tried to analyze the attack logs. We stood up the quantized version of ZAI's GLM-5.2 by Nvidia ( nvidia/GLM-5.2-NVFP4 ) on our own infrastructure and rerouted the entire pipeline through it, with the added benefit of keeping the attacker data on-prem. The first target was the dead-drop payloads: with GLM-5.2, we recovered the chunk+XOR+compress scheme and the per-campaign key the agent had itself leaked a

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t “anxiety” and a narrowing US-China gap. US Treasury Secretary Bessent floated sanctioning Chinese models over alleged AI “theft,” and White House official said Moonshot accessed Nvidia chips despite the export ban. Not everyone’s buying the panic. Independent researchers pushed back on US claims that K3 was built via distillation, and Deutsche Bank told clients not to expect Chinese models to displace US rivals anytime soon. (Bloomberg/SCMP/The Information) Riding that momentum, Moonshot is ra

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l far from that, we haven’t scaled to anywhere near that level. And scaling here includes scaling of data, scaling of model size, and training cost 05 Domestic Chips and Ecosystem Nvidia’s CUDA moat is being dismantled fast. Part of that is that we now have AI, and with AI, building up an ecosystem is far easier than before, because AI can write code; The compute-chip market is already bigger than the gaming-chip market, so there’s no reason for the two to stay coupled. The trend now is that the

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ks It won't kill you, or humanity, to get off the AI upgrade treadmill New models arrive so fast, we already have more new tech than it's possible to understand in a career Cisco, Nvidia team up to build fatter, faster pluggable If you thought 1.6 Tbps was fast, just wait, Cisco's USFP targets 12.8 Tbps in a single optical pluggable TOP STORIES Anthropic asks users to stop being mean to Claude; '123456' password linked to Denmark data breach affecting 8.8M people; Microsoft and Anthropic play in

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乎准备在此基础上发布更多模型。所以他们真的在推进。Reflection 永远处于“模型即将推出”的阵营，如果他们真的致力于开源，那么推出一些模型、一些代码或其他东西，让他们能够开始转动开发者飞轮，对他们确实大有好处。这需要很多——把模型发布出来很难。比如我和 Thinking Machines 的一些人聊过，感觉就像是，哦，实际做这件事工作量很大，我想。嗯，Nvidia 也在稳步推进。我认为他们现在是稳定的参与者。他们坚持发布模型。他们很快就会发布更多。他们发布大量数据。我在催他们尝试发布 Qwen 风格的小模型，就像 Gemma 那样。 Gemma 只有这些像 Qwen 竞品一样的模型，非常受欢迎。嗯，Gemma 模型有点——它们在尺寸上或针对这些尺寸的架构上有点杂乱，但 Gemma 模型在采用率方面确实真的能与 Qwen 模型匹敌。嗯，我不确定它们是否同样易于用于研究，这可能需要一段时间。可能需要多次迭代。比如现在语言模型研究的很大一部分都是围绕小型 Qwen 模型和基于 Qwen 的模型设计的，所以这需要时间。比如人们非常清楚如何使用这些模型，以及如何利用研究成果。所以我希望 G

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的中国模型制造商），这一事实对生成式AI模型授权和订阅的长期增长有着各种影响。当硬件价格高得惊人时，如果你必须在免费和非常昂贵之间做选择，你会选哪条路？ 奇怪的是，免费赠送并抢走其最大客户的生意，可能会在某个时候让英伟达与其最大客户产生矛盾，甚至惹上法律麻烦。这在很大程度上取决于美国和欧盟政府是否有意愿提起一项诉讼，指控英伟达利用其在AI硬件领域的垄断地位来为其免费的Nemotron模型提供资金支持。很难说特朗普政府会做什么、不会做什么，因为模型制造商和英伟达正时不时地与白宫套近乎，试图施加影响。据《华尔街日报》的一篇文章称，AI模型制造商们对市面上存在的开放权重和开源模型肯定不满，这些模型无疑是安全威胁，但同时也是对Anthropic、OpenAI和谷歌的竞争威胁，因为这些公司希望能够对基础模型的使用收费。 它们业务的未来取决于此。而从我们所能看到的情况来看，生成式AI模型和平台的实际收入与它们巨额投资所要求的水平相比，还相当微薄。 aws openai anthropic ai platform ai model ai gartner nvidia microsoft google

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ordings into a LeRobot dataset More Articles from our Blog LeRobot goes to driving school: World’s largest open-source self-driving dataset 109 March 11, 2025 robotics ai datasets NVIDIA's GTC 2025 Announcement for Physical AI Developers: New Open Models and Datasets +1 42 March 18, 2025 Community @ SteveNguyen @ glannuzel Congrats on the release, really excited to build one of these. One small snag while working through the BoM: the electronics repo it links to isn't publicly accessible. Could

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COMPUTE Jeff Burt Published mon 20 Jul 2026 // 15:52 UTC Nvidia scientists and their counterparts at a range of academic, scientific, and quantum computing institutions late last year published a research paper about the ongoing convergence of AI and quantum computing, and how each will play increasingly important roles in the development of the other. “There is ample intuition to motivate exploring AI as a breakthrough tool for [quantum computing],” the researchers wrote in the paper published

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e nor knock. Look at the teaser, and at the use cases it shows off, and it’s built for global, high-end developers and users more than for domestic users. It’s trained entirely on Nvidia, and unlike Zhipu and DeepSeek, K3 isn’t optimized for domestic chips from day one. It doesn’t carry the national-champion pressure that DeepSeek or Zhipu shoulder, the obligation to prop up the domestic ecosystem. If Moonshot were founded in Silicon Valley or Singapore instead of Beijing, you’d won’t find anyth

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