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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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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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