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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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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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appened to have a profitable and fast-growing cloud computing businesses are well behind us now. AWS is riding the GenAI boom high, and its own chips as well as those it buys from Nvidia, AMD, and Intel are in high demand for both AI workloads and traditional back office and data analytics stuff that makes the world go round. The company’s Trainium 2 and Trainium 3 capacity for the next two years is largely booked out, chief executive officer Andy Jassy said in his letter to shareholders in Apri

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ns/s on a phone, so a capable agent runs instantly and privately on your own device. GPU inference. We also measure output throughput (total output tokens / 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 0.5.16, 1,024 input tokens, up to 256 output tokens, in BF16, averaging 3 runs per concurren

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d’s top two AI labs, which insist on a closed-source approach and reportedly lobby for policies that could constrain open models. The other is a coalition of tech companies led by Nvidia, Microsoft and Meta, which are strongly in favor of open-weight models. They signed a letter last week warning against “premature restrictions” on open-weight models and launched an open AI security alliance, an organization building tools to defend against cyber attacks from frontier models. Both sides have jus

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O—one of China’s largest ever—sent shockwaves through the global chip trade, hitting Micron and SK Hynix shares and reviving the “China chip breakthrough” narrative that’s rattled Nvidia investors all year. Founder Zhu Yiming pledged $5.6 billion in worker bonuses out of his own windfall, and semiconductor-sector profits in China are reportedly up 2,500% in the first half on the AI boom. Nomura flagged room for the stock to keep rallying; the FT cautioned the surge “isn’t the bubble signal it mi

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n model finetuning service is making hundreds of millions in revenue per year and they’re releasing the best open-weight models built in the U.S.A. — ahead of the early leaders in NVIDIA with Nemotron and Arcee’s Trilogy. On the other side of the ecosystem is the sustained pace from the Chinese labs, with newer entrants like Xiaomi still accumulating mindshare in the broader AI economy. Having predicted consolidation for a long time, it now seems like a safer bet is to predict continued adoption

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Cm, Ascend NPU, XPU, CPU, and other backends. Special thanks to the Intel Neural Compressor and AutoRound contributors for their collaboration on AutoRound integration, and to the NVIDIA, AMD, ModelOpt, Compressed-Tensors, Quark, GGUF, and hardware-backend communities whose work continues to expand the range of efficient inference formats available to SGLang Finally, we thank everyone contributing to the SGLang quantization roadmap - from researchers proposing new numerical formats to maintainer

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