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rainer("lerobot local") returns a Trainer that works like create policy() , and a TrainSpec describes the run; the record-train-deploy loop then closes in a few lines: On a single NVIDIA L4 ( g6.4xlarge ), 500 optimizer steps of ACT (51.6M parameters, effective batch size 8) over a 120-frame episode completed in 133 seconds and wrote a checkpoint that create policy() loads back through the same entry point used to run any other policy. Training time scales with dataset size, batch size, and step

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$10.84 billion then, by the math, which is a 92.2 percent increase year on year. But, more than anything else, Supermicro has become a go-to maker of accelerated systems based on Nvidia CPUs, GPUs, and scale up and scale out switching, and that is a very large portion of that overall system sales at this point. But, in the fourth quarter, as Supermicro’s sales of AI systems, as it calls machines based on GPU (and maybe someday XPU) accelerators as well as CPU-only systems that are running agent

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d as one of the community’s best.\n\n“A Coin Flip for Safety” was reproduced using AlphaXiv’s OpenResearch with OpenCode’s free BigPickle model, with experiments run locally on 2× NVIDIA RTX 3090s.\n\nIt was a great demonstration of how agent-assisted research can make independent reproduction and auditing accessible with relatively modest compute.\n\nThanks to the Hugging Face and AlphaXiv teams for organizing the challenge and making the whole process open and reproducible!\n","html":" Really

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oken count: --no-split is the single fused kernel, --split is the split pipeline and is the default because it holds up across the whole sweep above. Benchmark Environment GPU: 8× NVIDIA B200 (P0, 1965 MHz SM, 3996 MHz HBM); Driver: 580.159.03; PyTorch: 2.13.0+cu130; CUDA (PyTorch build): 13.0; cuDNN: 9.19.0; Python: 3.14.6; OS: Ubuntu 22.04.5 LTS; CPU: Intel Xeon 6760P 9. Summary The fusion rests on two tricks. Folding gate and up into the tensor map places every SwiGLU pair in the same accumul

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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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sive inference on GPU. Because it answers directly instead of reasoning, it returns the first token quickly, even on image-heavy inputs. We measure time to first token on a single NVIDIA H100 SXM5 GPU, one request at a time, across three input types: a single 512x512 image, a 512x512 image with a 1,024-token input text, and 5 256x256 5-frame video clip. LFM2.5-VL-3B keeps latency consistently low and pulls far ahead on multi-frame inputs, returning the first token in about 34 ms on a 5-frame vid

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mands: the ability to see inside a model, adapt it, and retain the intelligence they build with it. This is why we are enthusiastic contributors to the Open Secure AI Alliance and NVIDIA Nemotron Coalition. We are now extending that openness beyond our own models. Mistral’s platform will support third-party open models, starting with Z.ai’s GLM-5.2. This and future open models will run on the same infrastructure, regional controls, and service commitments as Mistral models, so customers can broa

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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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ond half of my career and take part in some of that. Zhang Xiaojun: As I was researching this chip history, I found that ‘97 also had a big event in the chip industry—in April ‘97 Nvidia launched its third-generation chip, the NV3, and only then, after the failure of its first two generations, did it finally get a firm footing. Liao Heng: Did you notice that company at the time? No, I didn’t—I think back then they were a nobody. Actually, I think if we’re talking about chip history in ‘97, there

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nity where we expect Arista Networks to do well, particularly in switches using Broadcom’s Tomahawk 6 Ultra switch ASIC. But that market can only grow as fast as people stop using Nvidia’s NVLink and NVSwitch scale up networking and use other XPUs or compel Nvidia to adopt a cheaper and more standardized approach. (Both are likely until Nvidia ends up with maybe 65 percent of the market instead of damned near 100 percent.) Here is how Ullal cased the situation with scale up: “Where it's non-Nvid

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• 21 meta-llama/Llama-3.1-8B-Instruct Text Generation • 8B • Updated Sep 25, 2024 • 6.2M • 8.13k moonshotai/Kimi-K3 Image-Text-to-Text • 2.8T • Updated 30 days ago • 1.27M • 11.6k nvidia/NVIDIA-Nemotron-Labs-3-Puzzle-75B-A9B-NVFP4 Text Generation • 45B • Updated Jul 7 • 10k • 130 openai/gpt-oss-120b Text Generation • 117B • Updated Aug 26, 2025 • 4.48M • 5.34k zai-org/GLM-5.2 Text Generation • 753B • Updated about 1 month ago • 731k • 5.14k Papers mentioned in this article 1 Efficient Knowledge

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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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model and the speculative decoding drafter. You can use AutoModelForMultimodalLM and AutoProcessor classes to load the model and the processor. The same snippet runs unchanged on NVIDIA (CUDA), AMD (ROCm) and Intel (XPU) GPUs, device map="auto" places the model on whichever accelerator is available. Text-only Inference After loading the model, you can do text-only inference with it as follows. Prompting the model with images and text We would need torchvision to be able to use images and text.

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す。 モデルプールの側は、当初から入れ替え可能であることを前提に設計してきました。標準構成では最高性能を基準にモデルを選定していますが、用途によっては選定基準そのものを変えたいという要望があります。コストを優先したい場合、モデルの提供元の所在地を限定したい場合、実行環境を限定したい場合などです。この選定基準は構成側で選べるようにしており、その一環として先日のNVIDIAとの提携によりNemotronを利用できるようにする取り組みを開始しました。 検証：Gemma 4ベースの指揮者モデル 一方で、指揮者モデル自身のベースモデルの多様性はこれまで担保できていませんでした。そこで私たちは、これまでとは別系統のオープンモデルであるGemma 4（Apache 2.0ライセンス）をベースに、同じ訓練手法で指揮者モデルを訓練しました。同規模のモデルであれば、系統が違っても同じ手法が通用するのかを確かめることが目的です。 検証には、当社が独自に構築した評価用設問セット（知識を問う問題、コード修正、コード生成、大学院水準の科学の設問からなる）を用いました（これらの設問は訓練にも、訓練途中の候補選定にも

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ing news, alongside a fresh DeepSeek raise. The chip-export standoff kept escalating even as regulators moved on quieter fronts. Washington is reviewing China’s offshore access to Nvidia chips following recent Chinese AI breakthroughs. (Bloomberg); China tightened chip-design IP protections and clarified ownership rights, aiming to spur domestic semiconductor innovation. (SCMP); The EU’s AI Act transparency rules formally took effect. (36氪); China’s AI soft power is showing up furthest from home

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compute Jensen Huang, the chief executive officer and co-founder of Nvidia, let the cat out of the bag back at GTC 2026 in March that if hyperscalers, cloud builders, AI model builders, and other enterprises as well as sovereigns needed low latency inference if they want to use agentic AI or if they just want to charge a premium for chattybot inference, they could not do this on GPU architectures. What Huang showed was that to get the best low latency performance across a wider range of latency

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nth when AT&T said it was expanding its use of the vendor’s quantum technology after it was able to cut the time needed to process a network optimization job from about an hour to 15 seconds. 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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ago. That capex level is driven entirely by the Google Cloud backlog, which was at $514 billion as the second quarter came to a close, up 4.75X year on year and up 11.3 percent sequentially. 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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re already in place. What’s left is to extend them – before the platform becomes the bottleneck it was built to remove. Pankaj Gupta is senior director of private cloud solutions at Broadcom. 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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