数据中心用水挑战：从效率指标转向现实韧性考量
水资源正从数据中心的后台配套公用设施转变为核心瓶颈制约。随着行业发展，数据中心建设不能仅依赖平均效率指标，而需根据具体场地开展韧性规划，重点考量峰值用水压力、当地水文状况以及对周边社区的影响，以应对日益严峻的现实资源挑战。
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1微软发布新版Copilot超级应用：整合聊天、编程与智能体能力85 热度 ⌁2Anthropic 与 Akamai 达成 116 亿美元云算力协议，年内算力支出承诺已超 5000 亿美元82 热度 ⌁3马斯克披露AI算力布局：Colossus集群拟部署超百万张英伟达GB300芯片81 热度 ⌁4谷歌启动Project Suncatcher：计划下周开展首个太空轨道数据中心测试81 热度 ⌁5微软正式推出新版 Copilot“超级应用”，整合聊天、代码与智能体能力79 热度 ⌁9月24日2026-09-24
部署AI负载前如何全面验证GPU集群就绪性
在实际运行AI工作负载前，即便GPU集群通过了各项常规健康检查（如GPU单卡、网络链路及Pod状态均显示正常），512卡等大规模分布式训练任务仍可能面临运行失败。文章指出传统的基础硬件监控无法完全保障复杂AI任务的稳定运行，强调了在正式上线AI负载前，必须对GPU集群进行深度的端到端就绪性验证，以提前排查潜在隐患。
9月23日2026-09-23
同一屋檐下的十种隐患气体：数据中心内部潜藏的气体安全风险
现代数据中心为保障高可用性配备了不间断电源（UPS）电池、制冷系统及备用发电机等核心基础设施，但这些设备同时也会产生多种潜在的气体危险。这些隐藏的气体隐患不仅可能引发人身与设施安全事故，还可能导致昂贵的系统意外宕机，需要运维人员高度重视气体监测与防范措施。
英伟达推出Topograph：面向AI工厂的拓扑感知工作负载调度方案
随着AI工厂受限于功耗与互联网络瓶颈，系统最大化价值高度依赖全栈优化，其中GPU工作负载的调度放置成为核心环节。英伟达推出Topograph调度工具，通过感知底层硬件与网络拓扑结构，优化多GPU任务在集群中的分配方案，旨在消除网络通信拥塞并提升整体算力集群的运行效率与能效表现。
9月22日2026-09-22
英伟达推出面向数据中心电力与散热硬件的 DSX Ready 认证计划
英伟达本周正式推出了名为 DSX Ready 的全新硬件认证计划，主要面向数据中心的电力和散热基础设施设备。该计划旨在配合英伟达的 DSX AI 工厂蓝图，对用于构建下一代 AI 数据中心的供电及冷却系统进行兼容性与性能认证，以加速高效智算基础设施的落地部署。
液冷技术与NABERS：提升澳大利亚数据中心能效评级
施耐德电气专家撰文指出，随着澳大利亚数据中心运营商在有限的电力和冷却容量下部署更高密度的算力负载，冷却架构已成为直接影响设施能效评级的关键因素。文章分析了在澳大利亚国家建筑环境能效评级系统（NABERS）针对数据中心运行性能的考核背景下，运营商如何通过引入液冷等先进冷却架构来优化能源使用效率，进而改善数据中心的能效评级表现。
英伟达在Dynamo-Triton中集成TensorRT多设备推理，简化多GPU模型部署服务
随着生成式AI对计算与显存的需求不断超出单卡上限，英伟达推出TensorRT多设备推理新功能，并将其集成至NVIDIA Dynamo-Triton中。该方案旨在简化跨多个GPU的大模型推理服务部署流程，有效降低多卡分布式推理的工程复杂度，提升大规模生成式AI模型在多GPU环境下的服务效率与吞吐能力。
全球加速服务器销售增速进一步提升
行业最新动态显示，用于人工智能与高性能计算的加速服务器销售规模正在以更快的速度增长。随着全球对大模型训练与推理算力需求的持续爆发，搭载GPU、定制ASIC等加速芯片的服务器出货量与市场规模显著扩张。本条资讯基于标题信息整理，更详细的市场出货数据、厂商份额及具体增长指标等内容尚待进一步披露。
英伟达推出DSX Ready计划，认证AI工厂电力与冷却配套产品
英伟达推出DSX Ready计划，旨在为AI工厂认证适用的电力与冷却基础设施产品。随着AI基础设施规模持续扩大，电力、制冷、水资源、场地及电网等现实限制正在深刻影响建设者的部署决策。该计划帮助数据中心建设者选择与其计算架构相匹配的完整工厂设计产品，以克服基础设施瓶颈，更高效地将计算能力转化为实际的AI产出。
企业战略性采用托管数据中心以支持AI与混合云部署
随着人工智能应用的快速发展与业务扩展需求，越来越多的企业开始战略性地选择托管数据中心（Colocation）。企业借助托管服务来承载AI高密度负载、推进混合云架构部署，并在有效控制成本的同时获取增量算力容量与优质的网络连接性能，以满足当下对高弹性与高可用算力基础设施的紧迫需求。
9月21日2026-09-21
从“算力焦虑”到“算力理性”：Akamai解答AI推理时代的ROI之问
过去两年，全球AI产业的关键词是“抢GPU”。从Meta、微软到xAI，动辄十万卡级别的集群建设不断刷新着人们对算力规模的想象。在这一背景下，笔者有幸采访到了Akamai云计算首席技术官Jay Jenkins，围绕AI算力的分配逻辑、推理场景的隐性成本、分布式架构的合规价值以及多智能体时代的基础设施挑战，进行了深入探讨。
From Scholar to Leader: How We Build the Next Generation of Talent at Schneider Electric
Talent development is often thought of as something that happens in the future. At the Schneider Electric Hub in Novi Sad, Serbia, we see it differently: tomorrow’s leaders are shaped long before their first day at work. For more than two decades, we’ve partnered with... The post From Scholar to Leader: How We Build the Next Generation of Talent at Schneider Electric appeared first on Schneider Electric Blog .
9月19日2026-09-19
v0.34.3-rc1
server: allow registry cross-host redirects among allowlisted hosts (…
v0.30.0rc2
[Bugfix][NIXL] Avoid receive reports for notification-only requests (…
Digitalization is helping universities do more with less. Swansea University shows us how.
UK universities released 1.4 million tonnes of carbon dioxide last year, and the sector faces an estimated £37 billion bill to reach net zero—a bill that lands on institutions already stretched by tight budgets and rising costs. For most, decarbonizing isn’t a resourcing problem so... The post Digitalization is helping universities do more with less. Swansea University shows us how. appeared first on Schneider Electric Blog .
Amazon SageMaker Inference: 2026 year-to-date launches in review
Amazon SageMaker AI shipped 13 inference launches in year-to-date across two deployment paths: fully managed endpoints and Amazon SageMaker HyperPod Inference. This post reviews each launch, from inference recommendations and capacity-aware instance pools to tiered KV caching and disaggregated prefill and decode.
Introducing Kimi K3 on Amazon Bedrock
Kimi K3 from Moonshot AI is now available on Amazon Bedrock, giving you a powerful new open-weight option for coding and knowledge work. It offers native vision, a 1-million-token context window, and explicit prompt caching to reduce latency and input costs.
From pilot to production: Why enterprise AI needs an infrastructure strategy
For the past two years, enterprise AI has felt promising, but ethereal: thrilling to watch from a distance, but not something most companies could grasp themselves. We’ve all seen the demos, read the headlines, and over one billion of us now use standalone AI tools... The post From pilot to production: Why enterprise AI needs an infrastructure strategy appeared first on Schneider Electric Blog .
9月18日2026-09-18
How CSIRO built scalable, cost-optimized genomic variant querying on AWS
Learn how researchers at CSIRO, Australia's national science agency, built Serverless Beacon (sBeacon), a scalable serverless solution for securely querying genomic variant data on AWS. sBeacon implements the GA4GH Beacon standard using Amazon S3, AWS Lambda, Amazon DynamoDB, and Amazon Athena to support production-scale clinical and research applications.
AI in logistics: from visibility to orchestration
For years, logistics transformation has been built around visibility. Organizations invested heavily in dashboards, control towers, tracking platforms, and analytics to understand increasingly complex supply chains. Visibility remains essential, but it is no longer the end goal. The real question is what organizations do with... The post AI in logistics: from visibility to orchestration appeared first on Schneider Electric Blog .
How Equinix cut operational overhead with a shared services architecture on Amazon EKS
Equinix, the world's digital infrastructure company, built a shared services architecture on Amazon EKS to eliminate the operational sprawl of its self-managed Kubernetes environment. Learn how a multi-account North Star architecture centralized governance and shared services, delivering 4x faster deployments and 40% less operational overhead.
v0.34.2-rc2: mlxrunner: Release freed KV buffers during speculative decode
The decode loop releases MLX's pool of freed buffers every 256 generated tokens, which is also how often the KV cache grows and drops its previous, smaller buffers. The check fires only when the token count lands exactly on a multiple of 256. Speculative decoding emits several tokens per round, so most rounds step over the boundary and the pool is never released. Each growth at a long context leaves several GB of buffers that no later allocation can reuse, so the runner's footprint keeps climbing over a long generation until the system runs out of memory. We now release the pool whenever a round crosses a multiple of 256 tokens, which is what
Scaling Multi-GPU Video Captioning with PyNvVideoCodec and vLLM
How to leverage NVIDIA Hardware Video Decoders to Achieve Multi-GPU Scaling in Video Captioning and Description tasks.
9月17日2026-09-17
Building cloud-native PACS on AWS
A hybrid cloud architecture pattern for modernizing medical imaging on AWS. Learn how multi-hospital networks can centralize PACS archives, enable cross-facility interoperability, and use Amazon S3 storage tiers to manage cost and retention at scale.
How DHI Group accelerates generative AI workloads from idea to production using hackathons
Learn how DHI Group partnered with AWS to move generative AI workloads from idea to production using a structured hackathon. This post covers the Hackathon Acceleration Package, the winning ClearanceJobs and AgileATS agentic architecture on Amazon Bedrock AgentCore, and the principles that make hackathons a repeatable path to production.
What a 190% ROI on Network Modernization Looks Like
Enterprises are moving more data between more distributed endpoints, in more places, than ever before—which makes network modernization essential. Yet many leaders struggle to…
v0.30.0rc1: [Bugfix] Isolate supplemental FlashInfer BF16 autotuning (#57285)
Signed-off-by: jiahanc 173873397+jiahanc@users.noreply.github.com Co-authored-by: OpenAI Codex codex@openai.com
Helping bring the world’s first large-scale, near-zero emissions steel plant online
Video opens with archival footage of smoky, coal-fired steel factories, then transitions to clean, modern exterior shots of Stegra's new facility and a clear digital animation showing how green hydrogen replaces coal to produce near-zero emission steel.
proto-v0.3.0
Release vllm-proto 0.3.0
v0.34.2-rc1: mlxrunner: lay out model by contract, checkpoint and construction
model is one package with three jobs: the contract between the runner and the architectures, the opened checkpoint, and building nn layers from checkpoint tensors. Its files did not say which was which. base.go carried the folded package's name over the interfaces and the registry, root.go held the safetensors header scan next to Root, and quant.go mixed the nvfp4 global-scale helpers with quant parameter resolution. base.go becomes model.go, named for what it holds. root.go keeps Root and Open; TensorQuantInfo and the header scan join quant.go, so everything the checkpoint says about quantization is read and resolved in one file. The global-