チップと計算資源 — 今週のインテリア・暮らしのトレンド、194件 | Accept All すべて承認

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チップと計算資源

194件

Nikkei Asia Technology

Nikkei Asia TechnologyJapanJapan AI data centers set to quadruple by 2033 with $60bn investment

Nikkei Asia Technology

Nikkei Asia TechnologyJapanJapan's NEC scraps effort to build a quantum computer

Run Ray on TPU, Part 1: The foundations

Google Developers BlogLabsRun Ray on TPU, Part 1: The foundations Ray 2.55 introduces official, first-class support for Google Cloud TPUs, enabling developers to run distributed Python workloads on Google's accelerators using the familiar Ray task-and-actor APIs. To handle the strict n

Run Ray on TPU, Part 2: Ray AI libraries

Google Developers BlogLabsRun Ray on TPU, Part 2: Ray AI libraries This second installment explores how Ray’s higher-level libraries—Serve, Data, and Train—abstract the complexities of running AI workloads on Google's TPU slices. Ray Serve uses a simple topology configuration to correct

How to use Google microbenchmarks for evaluating TPU performance

Google Developers BlogLabsHow to use Google microbenchmarks for evaluating TPU performance Google's open-source TPU microbenchmark suite provides developers with granular performance metrics across Network, Compute, HBM, Host Transfer, and Attention components to validate real-world hardware capabilities. By l

Google Developers BlogLabsHeyGen x Google Cloud: Bringing Avatar IV to TPUs HeyGen ported their 18B+ parameter Avatar IV video generation model to Google Cloud's Trillium (v6e) TPUs via torchax and XLA, utilizing FSDP and Ulysses sequence parallelism across an eight-chip mesh. To achieve a 1.86x

Enterprise-Grade Precision for Long-Context Multimodal Embedding Inference on Cloud TPU

Google Developers BlogLabsEnterprise-Grade Precision for Long-Context Multimodal Embedding Inference on Cloud TPU Google Cloud has natively integrated TPU support into the vLLM serving engine, allowing developers to elastically scale high-demand embedding pipelines using Google Kubernetes Engine (GKE). To handle massive 15K+ token c

Qiita (LLM)JapanVRAMは束ねない。NVIDIA PAIRが家庭内PCを推論クラスタに変える仕組みローカルLLMを動かすPCを増やしても、1本の推論がその台数分だけ速くなるわけではない。 NVIDIA PAIRが減らすのは、複数の独立したリクエストが1台のGPUの前で並ぶ待ち時間だ。公式デモでは処理時間が18分から8分48秒へ短縮されたが、その数字を正しく読むには「...

Zenn (AI)Japan「Ollamaで動く」はずだったK2 Horizon — 16GB GPUで確かめたら、動いたのはvLLMだけだった IFMが2026-09-03に出したオープンモデル群「K2 Horizon」は、出どころから中身まで全部見せてくれる珍しいリリースだ。0.9B (超小型) から375B (超大型) まで6サイズ、ライセンスはApache 2.0。学習データのレシピ、途中のチェックポイント、強化学習のコードまで公開している。小型の0.9Bが数学のベンチマーク (AIME 2026) で48.5を取ったこと、「公表ベンチにごまかしがあったので自分で減点して

Nvidia returns to selling Founder's Edition RTX 50-series GPUs at MSRP in person at PAX West — Verified Priority Access has RTX 5090, RTX 5080, and RTX 5070 at list price

Tom's HardwareSiliconNvidia returns to selling Founder's Edition RTX 50-series GPUs at MSRP in person at PAX West — Verified Priority Access has RTX 5090, RTX 5080, and RTX 5070 at list price Nvidia is offering its RTX 5090, RTX 5080, and RTX 5070 Founder's Edition models at MSRP at PAX West.

LobstersGitHubRust SIMD on the GPU Comments

AMD reportedly prepping Ryzen 5 7500 (non-F) CPU with integrated graphics at double the price — Six-core Zen 4 chip rumored to share identical specs with its F-moniker cousin

Tom's HardwareSiliconAMD reportedly prepping Ryzen 5 7500 (non-F) CPU with integrated graphics at double the price — Six-core Zen 4 chip rumored to share identical specs with its F-moniker cousin A new report suggests AMD is preparing a non-F version of the Ryzen 5 7500F with integrated graphics. It would cost 230 Euros, or $267, which would put it above even the 7600X3D in terms of pricing, despite sharing ident

Modder gets Nvidia's DLSS 5 working on AMD's RDNA 4 GPUs — RX 9070 XT only manages 30 FPS at 1080p right now, but 5070 Ti-level performance is the eventual goal

Tom's HardwareSiliconModder gets Nvidia's DLSS 5 working on AMD's RDNA 4 GPUs — RX 9070 XT only manages 30 FPS at 1080p right now, but 5070 Ti-level performance is the eventual goal If you have an RX 9000 series GPU, you can try out DLSS 5 on your PC right now and absolutely destroy the stable performance you were getting before.

We tested DLSS 5 in NBA 2K27 with every RTX 50-series GPU — first official release comes with a big performance hit, but almost every Blackwell card can run it at 1080p

Tom's HardwareSiliconWe tested DLSS 5 in NBA 2K27 with every RTX 50-series GPU — first official release comes with a big performance hit, but almost every Blackwell card can run it at 1080p We tested Nvidia's DLSS 5 in NBA 2K27 across every RTX 50-series graphics card at 1080p, 1440p, and 4K to see just how much performance it costs to explore the frontiers of neural rendering.

b10819

llama.cpp releasesToolsb10819 metal : fix memory leak in early return ( #28399 ) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/45438612 macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (arm64, K

DLSS 5 officially launches inside NBA 2K27, limited to RTX 50-series GPUs for now — Nvidia promises to bring neutral rendering tech to RTX 40-series soon

Tom's HardwareSiliconDLSS 5 officially launches inside NBA 2K27, limited to RTX 50-series GPUs for now — Nvidia promises to bring neutral rendering tech to RTX 40-series soon Nvidia's controversial neural-rendering tech, DLSS 5, is now officially available in NBA 2K27, marking the start of a new era for the company. DLSS 5 will also come to RTX 40-series soon after current work on optimizing

TechCrunch AIValleyAI compute provider Nscale is looking for $3.5B in pre-IPO financing Nscale, which recently struck a $45 billion deal with Anthropic, is in talks to raise additional funds in anticipation of an upcoming IPO.

Building a Memory-Driven Agent with NVIDIA NemoClaw

NVIDIA Technical BlogSiliconBuilding a Memory-Driven Agent with NVIDIA NemoClaw Enterprise work spans messages, decisions, projects, and obligations that change over time. An AI agent that starts without this context must reconstruct it...

b10809

llama.cpp releasesToolsb10809 llama.cpp : bump version to 0.4.0 ( #28386 ) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/45314398 macOS/iOS: macOS Apple Silicon (arm64) macOS Apple Silicon (arm64, KleidiA

PyTorch BlogToolsYour Guide to Hardware Acceleration & Compute Infrastructure at PyTorch Conference North America 2026 TL:DR PyTorch Conference North America 2026 (San Jose, October 20–21) is packed with sessions on getting PyTorch to run fast, portably, and reliably across an increasingly diverse silicon landscape –...

Hacker News Show

Hacker News ShowGitHubShow HN: Open-Source eInk Bike Computer

Frontier Reasoning Reaches the Edge: How to Deploy and Optimize Models on NVIDIA Jetson

NVIDIA Technical BlogSiliconFrontier Reasoning Reaches the Edge: How to Deploy and Optimize Models on NVIDIA Jetson Running reasoning and agentic AI at the edge has been harder than it needs to be. Until recently, models capable of multi-step reasoning were too large to run...

Build a Physical AI model factory with NVIDIA Cosmos 3 on SageMaker HyperPod

AWS Machine LearningLabsBuild a Physical AI model factory with NVIDIA Cosmos 3 on SageMaker HyperPod Building a Physical AI system takes a continuous pipeline, not a single training job. This post shows how to run that model factory (synthetic data generation, post-training, and closed-loop evaluation with NVIDIA Cosmos

TechCrunch AIValleyApple’s Ternus era begins as Nvidia bets on the whole AI stack It’s officially the Ternus era at Apple. Tim Cook stepped down as CEO this week, handing the company to former hardware chief John Ternus, whose first memo promised a “huge launch next week” — timing that puts Apple’s ne

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Accept All is a magazine, a radar and a diary about artificial intelligence and the craft of code — read from Tokyo, for people who build.

The name is the button. Code is written by agents now and reviewed by a human thumb that hovers over Accept all a hundred times a day; the whole question of this decade is what, exactly, you just accepted. So we read the diff. A magazine, because every week one model, one tool or one idea is worth reading about slowly. A radar, because we count what the labs, the Valley, GitHub and the chip makers are talking about — releases, agents, open weights, compute, funding, rules — and show it as it moves. A diary, because a seal is set each day in that day's colour, and the days add up.

We read the research labs' own posts, the American and European tech press, the Japanese and Chinese developer sites and the newsletters people actually forward, and bring it together in English and Japanese. Everything here links back to the people who wrote it; the images belong to their authors.

Accept All will keep changing. New sources, new signals, new ways of looking — a little better each week, never finished. If something here made you open a terminal, that is enough.

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