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GPU 基礎設施動態

待翻譯:Sakana AI Launches Fugu Max and Fugu Ultra v2 for Cheaper, Stronger Multi-Agent Orchestration

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:Sakana AI has released Fugu Max and Fugu Ultra v2, 2 models built on the same learned orchestration architecture. Fugu Max routes tasks to lean open and specialized models, including NVIDIA Nemotron, at $2/$6 per 1M tokens. Fugu Ultra v2 targets peak capability, scoring 48.3 on Chartography and 74.3 on DeepSWE. The post Sakana AI Launches Fugu Max and Fugu Ultra v2 for Cheaper, Stronger Multi-Agent Orchestration appeared first on MarkTechPost.

MarkTechPost站內正文待翻譯:Sakana AI Launches Fugu Max and Fugu Ultra v2 for Cheaper, Stronger Multi-Agent Orchestration

待翻譯:NVIDIA Details BioNeMo Inference Runtime (BioIR): 2.90x Higher Boltz-2 Folding Throughput and 58.5K Residues per GPU-Hour on 8xH100

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:NVIDIA has detailed BioNeMo Inference Runtime (BioIR), a Python library that accelerates biomolecular structure-prediction models on NVIDIA GPUs while staying in plain PyTorch. In a matched benchmark on 1,000 human dimer targets across 8xH100 GPUs, BioIR-accelerated Boltz-2 delivered 58.5K successfully folded residues per GPU-hour versus 20.2K for a torch-compiled open-source implementation, a 2.90x gain. The runtime optimizes at 3 layers: custom kernel selection, CUDA Graph capture, and Ray-based replica scaling that places 1 full model copy per GPU. BioIR already powered the AlphaFold Database expansion, generating about 31 million candidate protein complexes across 4,777 proteomes. The post NVIDIA Details BioNeMo Inference Runtime (BioIR): 2.90x Higher Boltz…

MarkTechPost站內正文待翻譯:NVIDIA Details BioNeMo Inference Runtime (BioIR): 2.90x Higher Boltz-2 Folding Throughput and 58.5K Residues per GPU-Hour on 8xH100

待翻譯:Reduce inference cold starts on Amazon SageMaker HyperPod with model caching

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:Amazon SageMaker HyperPod now supports model caching for inference, which pre-loads model weights and container images onto cluster nodes so pods read from local NVMe storage instead of downloading over the network. Learn how model caching cuts cold starts from tens of minutes to seconds, how it works, and how to enable it.

AWS Machine Learning Blog站內正文待翻譯:Reduce inference cold starts on Amazon SageMaker HyperPod with model caching

待翻譯:Google to Invest $15B in Finland’s AI Infrastructure

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:The tech giant simultaneously revealed a nuclear power contract with Finnish operator Fortum, its first outside of the U.S.

AI Business站內正文待翻譯:Google to Invest $15B in Finland’s AI Infrastructure

待翻譯:Red Hat AI 3.5 tackles the GPU queue that can stall AI pilots

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:Red Hat released Red Hat AI 3.5 this week, a move designed to let software engineering teams run AI with The post Red Hat AI 3.5 tackles the GPU queue that can stall AI pilots appeared first on The New Stack.

The New Stack AI站內正文待翻譯:Red Hat AI 3.5 tackles the GPU queue that can stall AI pilots

待翻譯:Skild AI Taps NVIDIA Physical AI to Teach Robots New Tasks From a Single Video

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:Manufacturing floors, warehouses and production lines rarely stay fixed — tasks change, layouts shift and new products arrive, and most robots can’t keep up without significant reprogramming. Skild AI’s new S1 robot foundation model helps address this, designed to learn previously unseen, long-horizon tasks from a single video demonstration. The model, launched last week, uses […]

NVIDIA Blog站內正文待翻譯:Skild AI Taps NVIDIA Physical AI to Teach Robots New Tasks From a Single Video

待翻譯:Physical AI Takes the Wheel: How the World’s Robotaxi Leaders Are Building With NVIDIA Technologies

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:The global robotaxi market — physical AI’s first commercial breakthrough — is projected to reach $400 billion by 2035, with over 6 million commercial vehicles in operation as driverless fleets are already moving people through some of the world’s busiest and most complex streets. Deploying a driverless vehicle is one challenge. Scaling a fleet is […]

NVIDIA Blog站內正文待翻譯:Physical AI Takes the Wheel: How the World’s Robotaxi Leaders Are Building With NVIDIA Technologies

待翻譯:Universal Music is launching an AI music platform with ElevenLabs

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:Universal Music Group is launching a new AI-powered platform that will allow users to draw from its catalog of licensed music to create song remixes, mashups, and new takes on tracks, according to an announcement on Thursday. The record label is developing the platform through a multi-year licensing agreement with ElevenLabs, a company that specializes in AI voice and music generation. Artists can choose whether to participate in UMG and ElevenLabs' upcoming platform, which marks yet another AI deal for the record label. UMG is currently developing an AI music platform with Udio and has struck AI licensing deals with Spotify, Nvidia, and Kl … Read the full story at The Verge.

The Verge AI站內正文待翻譯:Universal Music is launching an AI music platform with ElevenLabs

待翻譯:d-Matrix Adopts NVIDIA NVLink Fusion for Rack-Scale XPU Deployment

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:AI inference chipmaker d-Matrix today announced it will use NVIDIA NVLink Fusion to connect its next-generation Raptor XPUs to NVIDIA’s AI infrastructure platform — joining a growing roster of ecosystem partners. By connecting Raptor to NVIDIA NVLink scale-up and Spectrum-X scale-out networking, the NVIDIA MGX rack architecture and the broader NVIDIA AI platform, NVLink Fusion […]

NVIDIA Blog站內正文待翻譯:d-Matrix Adopts NVIDIA NVLink Fusion for Rack-Scale XPU Deployment

待翻譯:Boots on the Ground: ‘WARDOGS’ Goes All Out on GeForce NOW at Early-Access Launch

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:Gear up: The latest PC games and major updates are ready to play on GeForce NOW this week. WARDOGS drops onto the cloud at early-access launch, alongside the Valheim 1.0 Deep North update and Bus Simulator 27 — part of nine new titles joining the cloud. The newest PC releases can demand serious hardware, storage […]

NVIDIA Blog站內正文待翻譯:Boots on the Ground: ‘WARDOGS’ Goes All Out on GeForce NOW at Early-Access Launch

待翻譯:“AI factories are among the most complex systems ever built”: Nvidia and Palantir turn Nvidia’s supply chain into a proving ground for sovereign AI

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:Nvidia and Palantir announced on Thursday that they’re working together to bring “sovereign AI to critical supply chains,” kicking off The post “AI factories are among the most complex systems ever built”: Nvidia and Palantir turn Nvidia’s supply chain into a proving ground for sovereign AI appeared first on The New Stack.

The New Stack AI站內正文待翻譯:“AI factories are among the most complex systems ever built”: Nvidia and Palantir turn Nvidia’s supply chain into a proving ground for sovereign AI

待翻譯:Introducing preemptible compute: the same compute, half the price

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:Together GPU Clusters now supports preemptible compute: the same GPU capacity at a flat 50% of the on-demand rate, with a five-minute drain window.

Together AI Blog站內正文待翻譯:Introducing preemptible compute: the same compute, half the price

待翻譯:To Infinity and Beyond: ThunderKittens Now on NVIDIA Vera Rubin NVL72!

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:We ported ThunderKittens to NVIDIA's Vera Rubin NVL72 and rebuilt our NVFP4 GEMM around the new hardware, taking it from 42% of roofline to over 22 PFLOPS — competitive with cuBLAS and CuTe DSL. Here is what changed in the ISA and how we used it.

Together AI Blog站內正文待翻譯:To Infinity and Beyond: ThunderKittens Now on NVIDIA Vera Rubin NVL72!

待翻譯:Deploying Qwen3.8-2.4T-A95B on Amazon SageMaker HyperPod with vLLM

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:Learn how to deploy Qwen3.8-2.4T-A95B, a 2.4-trillion-parameter open-weight model, on Amazon SageMaker HyperPod with vLLM. This walkthrough covers cluster provisioning, NVFP4 quantization, and an OpenAI-compatible endpoint with built-in reasoning, tool calling, and native MTP speculative decoding.

AWS Machine Learning Blog站內正文待翻譯:Deploying Qwen3.8-2.4T-A95B on Amazon SageMaker HyperPod with vLLM

待翻譯:Qualcomm Forges AI Chip Deal with Amazon

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:The chipmaker is competing with Nvidia, the world’s dominant producer of AI processors.

AI Business站內正文待翻譯:Qualcomm Forges AI Chip Deal with Amazon

待翻譯:NVIDIA Brings Real-Time AI to Broadcast, Sports and Global Streaming at IBC

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:At the IBC conference, running Sept. 11-14 in Amsterdam, the creative, technology and business communities are coming together to turn ideas into action and discuss innovations across the media and entertainment industries. More than 44,000 attendees from 170+ countries are gathering to explore 1,300+ exhibitions in 14+ halls and outdoor spaces, with over 600 speakers […]

NVIDIA Blog站內正文待翻譯:NVIDIA Brings Real-Time AI to Broadcast, Sports and Global Streaming at IBC

待翻譯:Simplify and support your TorchServe workloads using Ray Serve Deep Learning Containers

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:TorchServe is no longer maintained, leaving teams to own the entire GPU inference stack. The AWS Ray Serve Deep Learning Container is a supported, pre-tested container with the framework, GPU drivers, and serving layer already assembled. This post walks through deploying a vision-language model on Amazon EKS using the Ray Serve DLC on a single GPU node.

AWS Machine Learning Blog站內正文待翻譯:Simplify and support your TorchServe workloads using Ray Serve Deep Learning Containers

待翻譯:7 Approaches to Efficient LLM Training on Limited Hardware

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:Learn seven engineering techniques to train large language models on consumer GPUs without running out of memory.

KDnuggets站內正文待翻譯:7 Approaches to Efficient LLM Training on Limited Hardware

免費使用程式設計模型的 5 種方法

本文介紹五種無需付費訂閱或 GPU 即可使用 AI 程式設計代理與模型的方法:OpenCode Zen 的免費模型、ChatGPT 免費計劃中的 OpenAI Codex、Kilo Code 的 Auto Free、OpenRouter 的免費模型 API,以及 Google Antigravity 的 $0 計劃。包含安裝命令、使用要點與注意事項。

KDnuggets站內正文免費使用程式設計模型的 5 種方法

LLM 服務中的四種快取:KV、字首、提示詞與語義快取

隨著 LLM 應用日趨複雜,推理成本和延遲成為瓶頸。一個請求常包含系統提示、對話歷史、檢索文件與工具定義等海量 token,重複處理浪費算力。本文梳理 KV 快取、字首快取、提示詞快取與語義快取四種技術,分別說明它們如何在不同層面避免重複計算、降低成本並縮短響應時間。

Analytics Vidhya站內正文LLM 服務中的四種快取:KV、字首、提示詞與語義快取

Perplexity 詳解 GPU 嵌入服務棧:Ivy、Tulip 與 ROSE 如何支撐 pplx-embed

Perplexity 工程團隊發文介紹其嵌入模型 pplx-embed 背後的 GPU 服務架構。系統複用 LLM 推理核心,透過 Rust 閘道器 Ivy、推理伺服器 Tulip 和 Python 引擎 ROSE 協同工作,並藉助 CUDA Graph 與 LazyTensor 最佳化吞吐與延遲。文章還對比了不同注意力後端及與 vLLM 的基準測試結果。

MarkTechPost站內正文Perplexity 詳解 GPU 嵌入服務棧:Ivy、Tulip 與 ROSE 如何支撐 pplx-embed

Nous Research 為 Hermes Desktop 新增一鍵式本地模型設定

Nous Research 將 Hermes Desktop 的本地開源模型部署簡化為單擊操作:應用會讀取硬體、從目錄中挑選適配 GPU 的模型、下載權重並自動配置 llama.cpp,全程無需賬戶。系統採用4bit量化下限、至少64K上下文視窗的推薦模型,並按綠色/琥珀色/紅色標註每款模型的視訊記憶體適配情況。

MarkTechPost站內正文Nous Research 為 Hermes Desktop 新增一鍵式本地模型設定

微軟Project Zenith:面向開發者的“無干擾Windows體驗”

微軟為開發者最佳化的Windows體驗正式定名為Project Zenith,面向配備64GB及以上統一記憶體的新一代開發者裝置。AMD在IFA釋出了首款搭載Ryzen AI Halo晶片的迷你PC,後續還將有采用不同晶片的機型。裝置預裝VS Code、GitHub Copilot等工具,並預設關閉多項系統干擾功能,讓開發者可在本地不受計量限制地執行300億引數以上的模型。

The Verge AI站內正文微軟Project Zenith:面向開發者的“無干擾Windows體驗”

輝達129億美元收購Hugging Face:“AI界的GitHub”仍將保持開放

輝達宣佈以約129億美元收購AI模型託管平臺Hugging Face。為緩解外界對開放性和硬體中立性的擔憂,輝達承諾平臺將繼續支援多雲、多加速器生態,且不強制要求使用輝達算力。交易預計2027年上半年完成,尚需監管批准。

The New Stack AI站內正文輝達129億美元收購Hugging Face:“AI界的GitHub”仍將保持開放

Nvidia PAIR 讓你的閒置 Mac 和 PC 為 AI 智慧體工作

輝達推出開源軟體路由器 PAIR,可利用家中閒置的 Mac 和 PC 執行本地 AI 模型,加速智慧體工作流;它相容 Ollama/LM Studio,支援輝達 GPU 與 M4 晶片 Mac,現已開放測試。

The New Stack AI站內正文Nvidia PAIR 讓你的閒置 Mac 和 PC 為 AI 智慧體工作

輝達釋出免費 PAIR 工具:把閒置電腦變成個人 AI 資料中心

輝達推出免費開源軟體 PAIR(Personal AI Router),可將家庭網路中的閒置相容電腦連線起來,用於本地 AI 推理和智慧體(agentic)工作流。它支援 RTX 20 系列及以上、RTX Pro GPU、DGX Spark 系統以及 Apple M4 或更新晶片。PAIR 測試版即日起支援 Windows、Linux 和 macOS。

The Verge AI站內正文輝達釋出免費 PAIR 工具:把閒置電腦變成個人 AI 資料中心

《NBA 2K27》搭載 NVIDIA DLSS 5 領銜 26 款新遊戲本月登陸 GeForce NOW

GeForce NOW 九月新增 26 款遊戲,最大亮點是支援 NVIDIA DLSS 5 3D 引導神經渲染技術的《NBA 2K27》。此外,《吸血鬼:黎明行者》和《鬼武者:劍之道》也將在發售當天加入雲端遊戲庫。

NVIDIA Blog站內正文《NBA 2K27》搭載 NVIDIA DLSS 5 領銜 26 款新遊戲本月登陸 GeForce NOW

輝達宣佈收購 Hugging Face

輝達宣佈以129.303億美元收購 AI 開發者平臺 Hugging Face,並承諾保持其開放、多雲和多加速器的特性。Hugging Face 團隊將繼續保留品牌,為整個 AI 生態服務。

NVIDIA Blog站內正文輝達宣佈收購 Hugging Face

ZimaBlue:透過可擴充套件的影片預訓練演化通用世界行動模型

本文介紹ZimaBlue,一個從大規模影片中學習通用世界行動模型(WAM)的可擴充套件框架。它採用三階段訓練課程:首先在大規模人類和機器人自我中心影片上進行因果具身預訓練,然後透過統一動作表示的影片-動作中間訓練將視覺動態與異構機器人軌跡對齊,最後針對目標機器人進行專項微調。其非同步慢-快雙系統架構使得在NVIDIA RTX 4090上實現30Hz的即時動作預測。在真實機器人零樣本評估中,將訓練資料從僅目標機器人資料擴充套件到超過12萬小時的具身影片,成功率從36.1%提升至77.8%。ZimaBlue在多個基準測試中表現優異,尤其在未見任務上提升顯著。

arXiv Computer Vision站內正文ZimaBlue:透過可擴充套件的影片預訓練演化通用世界行動模型

CUDA-Harness:利用智慧體從自然語言生成和最佳化CUDA核心

CUDA-Harness是一個新框架,用於從自然語言生成和最佳化高效能CUDA核心。它引入了中間結構化生成以連線高層語義與低層核心生成,透過綜合驗證減少獎勵作弊,並提出了反饋自適應進化策略來優先保證正確性同時最佳化效能。實驗證明了其有效性及跨模型、硬體和C到CUDA遷移的泛化能力。

arXiv Computational Linguistics站內正文CUDA-Harness:利用智慧體從自然語言生成和最佳化CUDA核心

NVIDIA與CrowdStrike強化智慧體網路安全前沿

在CrowdStrike Fal.Con 2026大會上,NVIDIA CEO黃仁勳與CrowdStrike CEO喬治·庫爾茨共同釋出了SafeMind智慧體網路安全系統,該系統利用NVIDIA Nemotron模型和CrowdStrike資料,透過攻防對抗的持續進化迴圈,為防禦者提供前沿級AI安全能力。

NVIDIA Blog站內正文NVIDIA與CrowdStrike強化智慧體網路安全前沿

利用閒置算力,在AI熱潮中賺錢

隨著AI推理對算力的需求激增,多家公司開始利用家庭和小企業的閒置計算資源執行AI模型,並向裝置所有者支付報酬。這種分散式計算模式不僅成本更低、延遲更低,還能避免大型資料中心對社群和環境的影響。文章介紹了Far Labs、Evolving Edge等公司如何透過開源軟體和安全機制吸引使用者參與,並探討了這一模式的潛在優勢與挑戰。

IEEE Spectrum AI站內正文利用閒置算力,在AI熱潮中賺錢

輝達備受爭議的DLSS 5於9月3日釋出,且需要強勁的GPU效能

輝達本週正式釋出DLSS 5,儘管3月公佈時引發爭議。該技術現僅支援《NBA 2K27》,並需要高階的GPU效能,即使用RTX 5060也需要開啟6倍幀生成才能執行。

The Verge AI站內正文輝達備受爭議的DLSS 5於9月3日釋出,且需要強勁的GPU效能

Nemotron 3 Ultra 詳解:NVIDIA 的 550B 混合 Mamba-MoE 模型

本文詳細解析了 NVIDIA 於 2026 年 6 月釋出的 Nemotron 3 Ultra——一款 5500 億引數的混合 Mamba-注意力專家混合模型。該模型透過僅啟用約 550 億引數實現約 10% 的稀疏度,結合 Mamba-2 狀態空間層與 Transformer 注意力層,專為長時間多輪智慧體任務設計。文章介紹了 Nemotron 3 系列(Nano、Super、Ultra)的定位、混合架構的動機、訓練細節以及透過 OpenRouter、NIM 或 vLLM 進行呼叫的方法。

Hacker News AI站內正文Nemotron 3 Ultra 詳解:NVIDIA 的 550B 混合 Mamba-MoE 模型

待翻譯:Private AI search across your work

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:Private AI search across your work. Ask across Google Drive, Dropbox, and Slack. Get source-backed answers without a permanent index. Choose German hosting or your own infrastructure. Create free account Create your acc…

Hacker News AI站內正文待翻譯:Private AI search across your work

待翻譯:The AI moat isn't GPUs, it's the advanced packaging they require

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:The Hidden Bottleneck: Why Advanced Packaging is the Next AI Moat Executive Summary The market's obsession with GPU design is noise. The true bottleneck—and strategic moat—in the AI hardware race is not the silicon itse…

Hacker News AI站內正文待翻譯:The AI moat isn't GPUs, it's the advanced packaging they require

待翻譯:Nvidia and Semiconductor Vendor Expand Partnership

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:The move shows how the AI hardware giant is aiming to maintain its dominance, even through third parties.

AI Business站內正文待翻譯:Nvidia and Semiconductor Vendor Expand Partnership

待翻譯:AWS recognized as a Leader in The Forrester Wave: AI Infrastructure Solutions, Q4 2025

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:We're excited to share that AWS has been recognized as a Leader in The Forrester Wave: AI Infrastructure Solutions, Q4 2025. In this evaluation of 13 providers, AWS received the highest score in the Strategy category.

AWS Machine Learning Blog站內正文待翻譯:AWS recognized as a Leader in The Forrester Wave: AI Infrastructure Solutions, Q4 2025

待翻譯:Trump says datacenter opponents ‘want to end up being backwards and poor’

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:Three-quarters of Americans said in a recent poll that they oppose datacenters being built next to their homes Donald Trump has criticized communities pushing back against datacenter projects across the US amid a growing backlash, warning those that reject them risk becoming “backwards and poor”. As controversy surrounding local datacenter plans continues to swirl around election campaigns nationwide ahead of November’s midterm elections, the US president declared Americans “will only have yourselves to blame” if they are canceled. Continue reading...

The Guardian AI站內正文待翻譯:Trump says datacenter opponents ‘want to end up being backwards and poor’

待翻譯:Apple Is Suddenly an AI Infra Stock as OpenAI Buys 10k+ Macs

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:Investing Apple Is Suddenly an AI Infrastructure Stock as OpenAI Buys Macs by the Tens of Thousands OpenAI has been quietly buying Apple hardware by the tens of thousands, and it has nothing to do with iPhones or consum…

Hacker News AI站內正文待翻譯:Apple Is Suddenly an AI Infra Stock as OpenAI Buys 10k+ Macs

待翻譯:Speed Up LLM Inference with DSpark Speculative Decoding

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:Learn how DSpark speculative decoding can improve local LLM generation speed using the same GPU, with Qwen3-8B, llama.cpp, and CUDA.

KDnuggets站內正文待翻譯:Speed Up LLM Inference with DSpark Speculative Decoding

待翻譯:New York Governor Kathy Hochul thinks AI should be ‘less evil’

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:Today, I’m talking with New York Governor Kathy Hochul, and I’ll just warn you — this episode moves really fast. It’s an election year, after all, with a shocking amount of tech policy at stake, and Governor Hochul has taken strong positions on almost every major tech issue there is. For example, Meta just reached a settlement with dozens of states, including New York, which will restrict how teens use platforms like Instagram in very specific ways. Governor Hochul is a strong supporter of those restrictions and more, as you’ll hear. But those come with a cost — widespread age verification means adults will also have to show ID to use the internet, which will essentially make it impossible to be anonymous online. Verge subscribers, don’t forget you get exclusiv…

The Verge AI站內正文待翻譯:New York Governor Kathy Hochul thinks AI should be ‘less evil’

待翻譯:Beyond Data Scaling: Representation-Centric Continued Pre-training for Vision-Language-Action Models

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:arXiv:2608.27550v1 Announce Type: new Abstract: Scaling robot data is crucial for building generalist Vision-Language-Action (VLA) models, yet robot trajectories are harder to scale than web-scale image-text data because embodied collection is costly and sparsely covers the physical world. This makes representation quality a central bottleneck: under a fixed robot-data budget, continued pre-training must turn limited trajectories into transferable visual-action knowledge rather than merely fit actions. We propose VLAct, a VLA-oriented VLM backbone trained on broad, heterogeneous, multi-embodiment robot data before task-specific fine-tuning. VLAct preserves the broad VLM prior and encourages shared action semantics across embodiments through VLM-prior preservati…

arXiv Robotics站內正文待翻譯:Beyond Data Scaling: Representation-Centric Continued Pre-training for Vision-Language-Action Models

待翻譯:ABCD: Alpha-Composited Block Coordinate Descent: Constant-VRAM Training for Large Radiance Fields

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:arXiv:2608.27735v1 Announce Type: new Abstract: We present ABCD (Alpha-Composited Block Coordinate Descent), an out-of-core training framework for alpha-composited radiance fields, instantiated here for 3D Gaussian Splatting. Our method reformulates training as block coordinate descent over spatial partitions: only one block of parameters is active at a time, while all others are frozen. By exploiting the associativity of alpha blending, these inactive regions can be pre-rendered and collapsed into foreground and background RGBA images. As a result, for fixed partition size and image resolution, peak VRAM becomes O(1) with respect to total scene extent, rather than growing with full scene size. This enables GPUs with limited memory to train scenes that would ot…

arXiv Computer Vision站內正文待翻譯:ABCD: Alpha-Composited Block Coordinate Descent: Constant-VRAM Training for Large Radiance Fields

待翻譯:Quanta Perception as Probabilistic Events

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:arXiv:2608.27584v1 Announce Type: new Abstract: Autonomous systems rely on extracting information from light, yet remain brittle in extreme environments, from nighttime navigation to high-speed robotics. Conventional sensors aggregate photons over fixed exposures, imposing trade-offs between sensitivity, dynamic range, and temporal resolution that degrade perception when photons are scarce or dynamics are rapid. Quanta sensors detect individual photons, but their streams exceed real-time compute and latency budgets by orders of magnitude. Here we introduce $\textit{probabilistic events}$, a computational primitive for real-time quanta perception from individual photon detections. By computing the posterior over the time since the last intensity change, we repre…

arXiv Computer Vision站內正文待翻譯:Quanta Perception as Probabilistic Events

待翻譯:DAMP: Decay-Aware Mixed-Precision Recurrent-State Quantization

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:arXiv:2608.27513v1 Announce Type: new Abstract: Softmax attention stores key and value vectors for every preceding token, causing inference memory to grow with sequence length. Recent language models incorporating Gated DeltaNet (GDN) or Kimi Delta Attention (KDA) reduce this cost by replacing the KV cache in most layers with fixed-size recurrent states. However, these recurrent states are commonly stored in FP32 and consume substantial GPU memory; their updates are memory-bandwidth bound and contribute significantly to decoding latency. To our knowledge, we are the first to study post-training quantization of recurrent states in GDN and KDA based language models. We find that uniform quantization provides a poor accuracy--storage trade-off: INT8 and FP8 alread…

arXiv Machine Learning站內正文待翻譯:DAMP: Decay-Aware Mixed-Precision Recurrent-State Quantization

待翻譯:AI’s worst disasters will arrive unannounced | Letters

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:Dr Simon Nieder on the global efforts needed to curb the threats posed by AI. Plus letters from Dr Anthony Harris and David Kyler Timothy Garton Ash is right that serious artificial intelligence risks demand international action, but “AI Hiroshima” may be the wrong picture (Would even an AI disaster on the scale of Hiroshima be enough to make humankind protect itself? I fear not, 22 August). Hiroshima was not technology going rogue. Human beings designed the bomb, authorised its use and dropped it. The technology worked much as intended. AI may one day behave in ways we cannot control, but many of the gravest harms could happen without any dramatic moment when “the AI takes over”. AI might help design a pathogen, find a vulnerability in critical infrastructure…

The Guardian AI站內正文待翻譯:AI’s worst disasters will arrive unannounced | Letters

待翻譯:Show HN: FinBridge – Korean stock market data for AI agents (MCP)

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:Your agent reads EDGAR. It cannot read DART. Your model knows NVIDIA. It guesses about SK Hynix. Ask it about both. “Find KOSDAQ names above RS 90 that pass the trend template.” Ask Claude and it screens every Korean li…

Hacker News AI站內正文待翻譯:Show HN: FinBridge – Korean stock market data for AI agents (MCP)

待翻譯:Building Custom Batched Ensemble Weather Forecasting with NVIDIA Earth2Studio

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:In this tutorial, we build an ensemble weather forecasting workflow with NVIDIA Earth2Studio. We install the required Earth2Studio components while preserving Colab’s existing CUDA-enabled PyTorch environment, load the FCN prognostic model, and retrieve atmospheric initial conditions from GFS. We then implement a custom wind-power diagnostic that converts 10-meter wind components into turbine capacity factors, along […] The post Building Custom Batched Ensemble Weather Forecasting with NVIDIA Earth2Studio appeared first on MarkTechPost.

MarkTechPost站內正文待翻譯:Building Custom Batched Ensemble Weather Forecasting with NVIDIA Earth2Studio

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