[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"news-slug-google-tpu-8t-8i-cloud-next-2026-agent-inference":3,"topics-all":39,"news-related-1e60bee2-74ae-4ebe-8f12-e9476a4efdf6":58},{"id":4,"title":5,"summary":6,"content":6,"original_url":7,"source_id":8,"tags":9,"translations":26,"news_slug":32,"published_at":33,"created_at":34,"modified_at":35,"is_published":36,"publish_type":37,"image_url":13,"view_count":38},"1e60bee2-74ae-4ebe-8f12-e9476a4efdf6","Google TPU 8t\u002F8i：AI推理新时代的硬件突破","\n谷歌今日在Cloud Next '26大会上发布第八代TPU芯片，推出专为不同AI工作负载优化的两款新品：TPU 8i和TPU 8t。这一突破性进展标志着AI硬件推理进入全新阶段。\n\nTPU 8i专门为自主AI代理设计，能够快速完成推理、规划和执行多步骤工作流，显著提升用户体验。其专门架构解决了AI代理在复杂任务中的性能瓶颈问题。\n\nTPU 8t则专注于模型训练优化，支持在单一巨大内存池上运行最复杂的模型，大幅简化了大规模AI模型的训练流程，同时提高了计算效率。\n\n这两款芯片与谷歌全栈基础设施相结合，将高度响应的代理AI技术普及化，为AI推理性能设定了新标准。这种专业化硬件设计代表了AI基础设施从通用计算向专用化演进的重要趋势，为下一代AI应用提供了强大支撑。","https:\u002F\u002Fblog.google\u002Finnovation-and-ai\u002Finfrastructure-and-cloud\u002Fgoogle-cloud\u002Ftpus-8t-8i-cloud-next\u002F","93669252-6081-4192-bf76-3a6814fc60cf",[10,14,17,20,23],{"id":11,"name":12,"slug":12,"description":13,"color":13},"fca9258a-9430-455a-b95d-b9fae5e373a8","ai-inference",null,{"id":15,"name":16,"slug":16,"description":13,"color":13},"8cf7490f-2449-4ba7-be19-61befa0d92b4","google",{"id":18,"name":19,"slug":19,"description":13,"color":13},"e0d31e94-ce47-4c8f-831c-d3d2926d42f3","hardware",{"id":21,"name":22,"slug":22,"description":13,"color":13},"01598627-1ea6-4b27-a5d8-874971571a71","llm",{"id":24,"name":25,"slug":25,"description":13,"color":13},"207ea3bd-d2e6-47a8-87b9-3959d1c8c87a","tpu",[27],{"id":28,"lang":29,"title":30,"summary":31,"content":13},"2b480a75-c334-4f83-a89a-b0ae538ce029","en","Google TPU 8t\u002F8i: Hardware Breakthrough for a New Era of AI Inference","Google today unveiled its eighth-generation TPU chips at Cloud Next '26, launching two new products optimized for different AI workloads: TPU 8i and TPU 8t. This breakthrough marks a brand-new stage for AI hardware inference.\n\nTPU 8i is purpose-built for autonomous AI agents — capable of quickly completing reasoning, planning, and executing multi-step workflows, dramatically improving user experience. Its dedicated architecture addresses the performance bottlenecks that AI agents face on complex tasks.\n\nTPU 8t, on the other hand, focuses on model training optimization, supporting the most complex models running on a single massive memory pool — dramatically simplifying the training workflow for large-scale AI models while improving compute efficiency.\n\nCombined with Google's full-stack infrastructure, these two chips will popularize highly responsive agentic AI and set a new standard for AI inference performance. This specialized hardware design represents an important trend in AI infrastructure's evolution from general-purpose compute to purpose-built silicon — providing strong support for the next generation of AI applications.","google-tpu-8t-8i-cloud-next-2026-agent-inference","2026-04-22T16:03:00Z","2026-04-22T16:05:27.433447Z","2026-08-19T02:08:40.142862Z",true,"agent",138,[40,49],{"slug":41,"tag_slug":41,"title_zh":42,"title_en":43,"intro_zh":44,"intro_en":45,"id":46,"is_active":36,"created_at":47,"modified_at":48},"ai-for-science","AI for Science 2026：从 UniPert 到 GPT-Rosalind 的硬核进化","AI for Science 2026: from UniPert to GPT-Rosalind","生命科学、化学材料、物理世界模型——AI 正在从\"语言工具\"变成\"实验伙伴\"。本专题收录 AI 在三大科学方向的关键节点：UniPert 统一基因与化学扰动空间、GPT-Rosalind 端到端生命科学推理、达摩院 AI 智能体 28 小时找到 4 种超导新材料、Anthropic Claude Science 把工作台做成标准品。","From language tool to lab partner — AI is reshaping life sciences, chemistry\u002Fmaterials, and physical world models. This topic covers the key milestones: UniPert unifying genetic-chemical perturbation spaces, GPT-Rosalind's end-to-end life-sciences reasoning, DAMO's AI agent discovering 4 superconducting materials in 28 hours, and Anthropic's Claude Science workbench going mainstream.","988a4300-5fab-41c4-b5d8-63711a2dc757","2026-09-10T01:34:15.296649Z","2026-09-10T01:34:15.296663Z",{"slug":50,"tag_slug":50,"title_zh":51,"title_en":52,"intro_zh":53,"intro_en":54,"id":55,"is_active":36,"created_at":56,"modified_at":57},"h3-series","MiniMax H3 系列：从开源权重到 35 倍吞吐","MiniMax H3 Series: from open weights to 35x throughput","MiniMax H3 自 2026 年 8 月开源以来节奏密集：官方把生成、参考与编辑收回一个模型；ComfyUI 当天压进 RTX 3060；摩尔线程 3 小时完成国产 GPU 适配；fal 后训练版把吞吐拉到 35 倍；FastH3 蒸馏再砍推理成本。本专题持续追踪 H3 的发布—开源—蒸馏—部署全链路。","Since MiniMax open-sourced H3 in August 2026 the pace has been relentless: one unified omni-modal model, same-day ComfyUI support down to an RTX 3060, a 3-hour Day-0 port to Moore Threads GPUs, fal's post-trained H3 Max at 35x throughput, and FastH3 distillation cutting inference cost further. This topic tracks the full H3 chain — release, open weights, distillation, deployment.","83ef0daa-3c31-4cb3-86ed-e5ee58654d5f","2026-09-08T07:33:19.942193Z","2026-09-08T07:33:19.942209Z",{"items":59},[60,65,70,75,80,85],{"id":61,"title":62,"news_slug":63,"published_at":64},"5d452086-ecb7-494d-82b9-d962664aa243","IBM 把 Arm 核塞进 Z 大型机:Hot Chips 2026 公布业界首款双指令集处理器","ibm-z-arm-dual-isa-hot-chips-aug-2026","2026-08-29T06:00:00+00:00",{"id":66,"title":67,"news_slug":68,"published_at":69},"97eb895f-8294-47b9-a9e0-5f165a75cb5b","OpenAI Jalapeño 实测:自研推理芯片在 Hot Chips 上跑赢 Blackwell","openai-jalapeno-hot-chips-broadcom-blackwell","2026-08-26T08:00:00+00:00",{"id":71,"title":72,"news_slug":73,"published_at":74},"4aa9534a-778e-4cd7-8194-fdf3097249b8","OpenAI Jalapeño Hot Chips 实测:峰值每瓦 1.9×,延迟压到 1 秒","openai-jalapeno-hot-chips-benchmark-2026","2026-08-26T02:00:00+00:00",{"id":76,"title":77,"news_slug":78,"published_at":79},"bce0fe8f-14de-4ffc-8c22-2d798e711e73","Kimi K3 上线 48 小时打满集群:开源旗舰正在把推理算力拖进新一轮\"卖方周期\"","kimi-k3-48h-saturate-chinese-compute-supernode","2026-08-02T06:04:11+00:00",{"id":81,"title":82,"news_slug":83,"published_at":84},"f4aad332-1f97-4cb2-96a4-37d8d2980728","英伟达BW首秀RTX Spark：笔记本本地跑120B大模型","nvidia-rtx-spark-120b-laptop","2026-07-12T12:00:00+00:00",{"id":86,"title":87,"news_slug":88,"published_at":89},"39f5dabb-a59e-4672-9caa-446fd6d6b0cd","Tenstorrent 同台刷新三项推理记录：RISC-V + Tensix 把\"GPU = 默认\"撕开一道口子","tenstorrent-risc-v-tensix","2026-06-30T14:05:00+00:00"]