搜索“NVIDIA”

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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a switch fabric. The GPU-to-GPU Infinity Fabric link supports 100 GB/s bidirectional bandwidth, and the GCD-to-GCD Infinity Fabric link supports 400 GB/s bidirectional bandwidth; NVIDIA A100 / HGX A100: NVLink through NVSwitch. HGX A100 uses eight A100 GPUs and six NVSwitch chips. Each A100 has 12 NVLink ports, and each NVSwitch is a non-blocking switch connected to all eight GPUs. As a result, any A100 can communicate with any other A100 at full NVLink bidirectional speed, up to 600 GB/s per G

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Building on SGLang's serving framework and AMD's MoRI communication library, we demonstrate that AMD achieves competitive — and at key operating points, superior — TCO compared to NVIDIA B200 running Dynamo + TRT-LLM. These results are validated by InferenceX, SemiAnalysis's open-source continuous benchmark platform that tests across hundreds of GPUs with a live dashboard. This post describes what we achieve, how we achieve it, and our plans for the road ahead. TL;DR At 129 tok/s/user interactiv

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nference. We support inference via vLLM and SGLang via active contributions to these codebases. We measure output throughput (total output tokens divided by wall time) on a single NVIDIA H100 SXM5 GPU using a sustained-load setting: at each concurrency level, we continuously maintain the target number of in-flight requests, replacing each completed request immediately. We benchmark each model with SGLang 0.5.12, 1,024 input tokens, up to 256 output tokens, in BF16, averaging 3 runs per concurren

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High end NVIDIA cards, and the server and power needed to run them, cost a lot of money, especially if you plan to reach enough VRAM to run massive models. The alternative, so far, has been Apple hardware, or the DGX Spark that, even if severely limited because of memory bandwidth, still allows to run LLMs prompt processing (prefill) fast enough. The Mac Studio provided up to 512GB unified memory, a solution with modest memory bandwidth (but much better than the Spark) and compute at a price tha

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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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AI chip. We’re going to build up from here to how you would use it to run all the other operations you might want. Dwarkesh Patel This might be the wrong time to ask, but whenever Nvidia reports that this chip can do X many FP4 or half as many FP8, it seems to imply those circuits are fungible, that there’s not a dedicated FP4 versus FP8. But the way you’re mapping it out here, it seems like if it has to be mapped out in the logic, you would need a dedicated FP4 multiply-accumulate and then a de

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at $1.5 per million input tokens and $7.5 per million output tokens. Open weights are on Hugging Face under a modified MIT license. It is also available for prototyping, hosted on NVIDIA GPU-accelerated endpoints on build.nvidia.com and as a scalable containerized inference microservice, NVIDIA NIM. Build the future of agentic systems with us. We're hiring across research, engineering, and product to push agentic systems further. See our open roles. Mistral Medium 3.5 Modified MIT Merged dense m

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ntarily ended the session. In addition to achieving strong kernel generation results on PPUs, Qwen3.7-Max also generates high-quality, production-grade kernels across a variety of NVIDIA GPUs. For example, on KernelBench L3, Qwen3.7-Max is able to produce accelerated kernels for 96% of the scenarios, compared to 98% for Opus-4.6, 78% for GLM 5.1, 80% for Kimi K2.6, 54% for DeepSeek V4 Pro, and 48% for Qwen3.6-Plus. This result highlights two properties of Qwen3.7-Max as a foundation model poweri

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ore of their AI-generated content. This builds on our ongoing work to make content origins clearer, like open-sourcing our SynthID text watermarking technology and partnering with NVIDIA to watermark AI-generated video from their Cosmos world foundation models . To help more organizations identify AI-generated media, we’re launching a new AI Content Detection API on Google Cloud’s Gemini Enterprise Agent Platform. This gives businesses a powerful way to spot AI content made by both Google and ot

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r on the open-weight market. It is developed by Zyphra, and one of the interesting details around the release is that the model was trained on AMD GPUs rather than the more common NVIDIA GPU (or Google TPU) setup. The main architecture detail, though, is Compressed Convolutional Attention (CCA), used together with grouped-query attention. Unlike MLA-style designs that mainly use a latent representation as a compact KV cache format, CCA performs the attention operation directly in the compressed

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