搜索“NVIDIA”

of the more copied ideas after DeepSeek-R1. Qwen swaps standard quadratic attention for a linear network on every other layer, cheaper and longer-context, at some cost to quality. NVIDIA goes furthest, replacing the transformer on a fraction of its layers with state-space models that scale linearly and compute faster, keeping enough transformer layers to hold accuracy up. With architecture-level changes like these, the ladder reaches roughly a hundred and forty users per GPU. Nothing here is fre

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ert Yuksekgonul, Daniel Koceja, Xinhao Li, Federico Bianchi, Jed McCaleb, Xiaolong Wang, Jan Kautz, Yejin Choi, James Zou, Carlos Guestrin, Yu Sun. With collaborators at Stanford, NVIDIA, UC San Diego, and the Astera Institute. Paper: arXiv:2601.16175 How you train and shape a model: reasoning, fine-tuning, and reinforcement learning. Paper 4 Escaping the Verifier: Learning to Reason via Demonstrations (RARO) 25% vs 5.9% vs experts · no verifier A 25% win rate against expert responses, versus 5.

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y side on the same machine without having to switch manually back and forth between models Regarding the second point in the list above, that Qwen models work better in Qwen-Code, Nvidia’s Polar: Agentic RL on Any Harness at Scale paper (May 2026) has a benchmark showing that the Qwen3.5-4B base model has the best coding performance in said Qwen-Code harness (both before and after their Polar-RL training), which I included below. The benchmark in the table above is for an older Qwen3.5 model, an

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LLM serving engine How does the above TTFT reduction translate into improvements in end-to-end LLM inference? To answer this question, we run the end-to-end LLM serving benchmark, NVIDIA AIPerf. We use workloads that represent agentic AI inference, consisting of 1K tokens of system prompt, 20K tokens of per-user context. Each user inputs 26 tokens in a single turn, and the average number of turns is 20. Solid points indicate Pareto-optimal points; shaded points indicate non-Pareto-optimal ones.

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ings in-house, but we didn’t make this decision lightly. We were focused on supporting LLM inference, but not to the exclusion of other workloads, so we couldn’t use a system like NVIDIA Dynamo or llm-d. We have lots of production experience operating AWS NLB and nginx, but we needed a distinct feature set — for instance, handling rapid churn in upstreams as users create & destroy services (and services create & destroy replicas). fprs makes it easy for us to ship what we need We built fprs usin

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while remaining competitive with larger models. As an early look at ongoing work, we deployed LFM2.5-230M on a Unitree G1 humanoid robot, running entirely on-device on its onboard NVIDIA Jetson Orin. Here the model acts as a skill-selection layer: it takes a single natural-language instruction and decomposes it into a sequence of tool calls that invoke pre-trained low-level skills provided by NVIDIA's SONIC framework. After a quick fine-tune for this task, the model turns a free-form command suc

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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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line. We'll cover why they fail, what the patterns look like, and a few cases where models surprisingly produced kernels faster than anything publicly available, including one for NVIDIA NeMo-RL's GRPO training loop , which has no prior optimized public reference. LLMs have made progress on GPU kernel generation, but that progress has mostly been measured on a single GPU. Production AI workloads no longer fit that frame: they span multiple GPUs, and performance is increasingly shaped by communic

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于前代产品的网络性能，但能力肯定是相关的。若一切不变，这指向一种可能性：美国政府认定某个特定的中国开源权重模型对公众不安全。这里还有许多其他潜在情景，但清楚的是，我们在梳理这些情景、准备我们的基础设施以及向社会传递信息方面还有大量工作要做。 要为一个能力日益强大的开源模型世界构想并向决策者传达管理之道，需要远不止我一个人。2 我们未来还有多年的 AI 进步，Nvidia 的下一代芯片已投产，算法进步源源不断。对开源模型倡导者来说，这感觉是一条狭窄的道路，但我们需要弄清楚如何让它们可行，这样性能的巨大飞跃才不会只流向闭源模型。 我完全理解，想象一个可公开获取的 Mythos 级模型为何令人恐惧，但如果开源模型现在被禁，而只有闭源模型在两年内于一两家公司手中变好 10 倍或 100 倍，我认为我们手上的问题会更大。 一直让我印象深刻的是中国实验室发布模型的速度。我从多个实验室听说，模型训练完成后公开上传权重到 HuggingFace 的时间可以用小时而非天来衡量。这一点至少有所放缓，因为他们现在需要准备将模型服务于更广泛的推理市场。 需要更多讨论的一点是，即便是闭源模型，例如 Mythos

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business would have grown dramatically in the first quarter. Non-X86 machines, dominated by the homegrown Arm processors created by the hyperscalers and cloud builders as well as Nvidia systems based on its “Grace” Arm server CPU, accounted for $58.7 billion in gear, up by a factor of 2.1X year on year and up 5.8 percent sequentially. X86 systems had 52.1 percent revenue share in Q1 2026, while non-X86 machines had 47.1 percent share. IBM’s Power Systems RISC machines and System z mainframes ac

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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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for over half of new CPU capacity deployed over the past three years. Microsoft and Google have followed with their own Arm-based designs, including Azure Cobalt and Axion, while NVIDIA’s Grace and Vera signal that it sees Arm as central to the future of AI infrastructure. Now about half of the compute shipped to top hyperscalers are Arm-based platforms. Purpose-built for customers Hyperscalers are not only deploying Arm processors but also designing silicon and infrastructure together to refle

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and unconstrained natural-language instructions without any environment-specific fine-tuning. Benchmarking Qwen-RobotNav Deployment Deployed zero-shot on a Unitree Go2 quadruped (NVIDIA Jetson Thor, 196ms latency) using only the built-in low-resolution camera. The robot executes step-by-step verbal instructions across multiple rooms in a previously unseen apartment. Instruction Following We evaluate a back-and-forth navigation task in an unseen exhibition hall: the robot first navigates 21.78 m

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MaxProof. From Gemini Deep Thinking reaching gold-medal level on IMO 2025, to DeepSeek-Math-V2 becoming the first open-source model with gold-medal-level capability, to SU-01 and NVIDIA Nemotron Cascade2 showing specialized math-contest ability in smaller models, and GPT 5.5 solving long-standing open problems, models are steadily entering harder regions of the mathematical problem space. During the iteration from M2 to M3, we also worked to integrate stronger mathematical proof and self-improv

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xts. That being said, 120B-A12B may be a bit too large for local inference on regular consumer hardware, but there is a Nemotron 3 Nano (4B) version as well. Note that 2 days ago, Nvidia also released a scaled up-version of this, Nemotron 3 Ultra (550B-A55B), which scales the embedding and projection dimensions but otherwise uses the same building blocks. If you are interested in a visual, I posted about it on Substack Notes here. This hybrid-architecture trend with alternating attention and alt

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spree, or maintain it, or accelerate it at the expense of legacy IT systems. The only way to predict what will happen for sure is to live through it. So, hang on to your hard hats, everyone. SPONSORED FEATURE: HPE and NVIDIA help organizations apply accelerated computing, software, enterprise infrastructure, networking, control plane, and services to the workloads and business outcomes that matter most. October 1, 2026 September 30, 2026 AMD Buys World Labs For $8.2 Billion To Get AI Model Cred

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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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e, the total token cost for proprietary models at the frontier is flat or increasing, thanks to increasing reliance on test-time compute. Fortunately, multiple organizations, from NVIDIA and Google to DeepSeek and Kimi, are releasing excellent models under permissive licenses. While small (here, under a billion parameters) models still have strong fine-tuning potential, for more complex tasks, larger models are the move. They tend to have a higher capability ceiling, better data efficiency, and

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optimize. Engineers must simultaneously handle multiple tightly coupled issues, including data layout, compute pipeline scheduling, and adaptation to hardware characteristics. On NVIDIA Hopper architecture GPUs, hand-writing a production-grade FP8 GEMM kernel typically requires one to two weeks of focused effort from an experienced team. We used this task to evaluate M3’s long-horizon autonomous iteration capability. We asked MiniMax M3 to optimize this kernel on NVIDIA Hopper architecture GPUs

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