23:30
英伟达两年从零建起近千亿美元股权投资组合据《商业内幕》9月4日报道,英伟达最新财报显示,截至7月26日公司持有价值990亿美元的股权投资,一年内增长14倍、两年增长45倍。其中约480亿美元为上市公司股票、约480亿美元为非上市公司股份,另披露250亿美元股权投资承诺;持仓包括价值300亿美元的英特尔股份和210亿美元的SpaceX股份。
推荐理由:原文整理了英伟达近千亿美元股权投资的构成与增速,并呈现质疑方看法,便于读者判断其投资扩张的规模与争议。
00:04
Artificial Intelligence News(网页)精选
NVIDIA 以 129.3 亿美元收购 Hugging FaceNVIDIA 已同意以 129.3 亿美元收购 Hugging Face,以扩展该开源模型仓库的平台与基础设施。交易目标是平台增长和基础设施投资,旨在为全球企业开发者、软件工程师和研究机构扩大 AI 访问机会。Hugging Face 由 Clem Delangue、Julien Chaumond、Thomas Wolf 等人在过去十年间打造。
另有 1 家信源报道Hacker News 热门(buzzing.cc 中文翻译)
推荐理由:原文报道 NVIDIA 收购 Hugging Face 的金额与用途,读者可以据此了解开源模型平台归属变化的方向。
20:09
NVIDIA 宣布以 129.303 亿美元收购 Hugging FaceNVIDIA 宣布已同意以 12,930,300,000 美元收购 Hugging Face,黄仁勋在官方博客公布了这一消息。Hugging Face 目前有超过 1800 万开发者,托管超过 300 万个模型、50 万个数据集和 100 万个应用,服务超过 20 万家企业。
另有 18 家信源报道X:Clément Delangue(Hugging Face CEO) (@ClementDelangue)X:Kim (@kimmonismus)X:Elvis Saravia (@omarsar0, DAIR.AI)Hacker News 热门(buzzing.cc 中文翻译)X:Rohan Paul (@rohanpaul_ai)X:Sundar Pichai (@sundarpichai)X:Satya Nadella (@satyanadella)IT之家(RSS)X:Aravind Srinivas(Perplexity CEO) (@AravSrinivas)The Decoder:AI News(RSS)The Verge:AI(RSS)Ars Technica:AI(RSS)X:Thomas Wolf(Hugging Face 联创/CSO) (@Thom_Wolf)X:Jensen Huang (@JensenHuang)X:Peter Steinberger (@steipete)X:Aidan Gomez(Cohere CEO,@aidangomez)X:Emad Mostaque (@EMostaque)TechCrunch:AI(RSS)
推荐理由:原文来自收购方本人,给出了收购金额和对平台开放性的具体承诺,读者可以据此评估对开源生态的影响。
06:55
Hacker News 热门(buzzing.cc 中文翻译)精选
英伟达预计 2028 财年销售额达 6730 亿美元英伟达预计 2028 财年营收增长 70%,年销售额约达 6730 亿美元,将超过苹果和 Alphabet,仅次于亚马逊。CFO Colette Kress 于 8 月 26 日给出该预测,远高于分析师平均预期的 44%。供应而非需求成为近期上限,黄仁勋称内存等部件短缺限制了更高预期,客户群正从超大规模厂商扩展至 ACIE。
推荐理由:英伟达的预测重点在于客户结构从超大规模云厂商扩展到区域 AI 公司和初创企业,同时其融资支持客户的方式形成循环资金依赖,这比单纯销售额数字更能反映其长期增长模式。
10:54
NVIDIA 季度营收指引达 1080 亿美元,首次突破单季千亿大关NVIDIA 上季度营收 960 亿美元,同比增长 106%,并指引 Q3 营收达 1080 亿美元(±2%),成为首家单季营收破千亿的半导体公司。按此年化营收 4320 亿美元,NVIDIA 已跃居全球第六大公司。同时,非超大规模客户首次贡献数据中心净新增收入的大部分,应收账款周转天数从 45 天升至 60 天,显示其正通过延长付款条件为买家提供更多供应商融资。
另有 2 家信源报道IT之家(RSS)X:Rohan Paul (@rohanpaul_ai)
推荐理由:营收破百亿之外,DSO 从 45 跳到 60 是一个值得跟踪的信号,它提示 NVIDIA 正通过放宽信用支撑非超大规模客户,可能改变市场对其收入质量的判断。
08:25
英伟达预计 2028 财年营收同比增 70%,黄仁勋称实际需求远高于此英伟达预计 2028 财年销售额同比增长约 70%,CEO 黄仁勋称实际市场需求远高于这一数字,增速主要受供应能力限制。公司同时宣布将在 2027 至 2028 年向 AWS 额外供应 200 万块 GPU。第二季度营收同比增长 106% 至 962.2 亿美元,连续第 13 个季度创下营收纪录。
另有 1 家信源报道IT之家(RSS)
推荐理由:黄仁勋把需求分成超大规模云与尚被低估的主权 AI 两类,只按云厂商资本开支外推 AI 需求会漏掉正在扩大的企业端采购。
07:58
亚马逊将英伟达芯片订单增至三倍,新增200万颗GPU亚马逊与英伟达宣布扩大合作,将在2027和2028年为AWS数据中心新增200万颗GPU芯片,包括Blackwell Ultra、Rubin和Rubin Ultra。此前五个月亚马逊刚同意部署超100万颗英伟达GPU,英伟达称此后“需求超出预期”。双方未披露财务条款,但按GPU单价估算交易价值达数百亿美元。
另有 1 家信源报道IT之家(RSS)
推荐理由:亚马逊在自研Trainium的同时三倍追加Nvidia订单,显示短期AI算力缺口仍大于自研替代,这会影响市场对云厂商自研芯片能否替代Nvidia的判断。
07:25
英伟达 2027 财年半年报归母净利润 1180.1 亿美元,同比增长 161.1%英伟达发布 2027 财年半年报,上半年营收 1778.37 亿美元,归母净利润 1180.1 亿美元,同比增长 161.1%,GAAP 毛利率 75%。第二财季营收 962.21 亿美元,同比增长 106%,环比增长 18%,归母净利润 596.88 亿美元,同比增长 126%。数据中心业务第二季度收入 890.23 亿美元,同比增长 117%,Vera Rubin 平台已进入全面量产。
另有 4 家信源报道The Verge:AI(RSS)X:Rohan Paul (@rohanpaul_ai)X:Kim (@kimmonismus)X:阑夕 (@foxshuo)
推荐理由:数据中心收入同比翻倍叠加 Vera Rubin 平台全面量产,显示 AI 基础设施的资本开支仍在加速,延迟部署的概率下降。
08:00
NVIDIA 季度营收指引达 1080 亿美元,首次突破单季千亿大关NVIDIA 上季度营收 960 亿美元,同比增长 106%,并指引 Q3 达 1080 亿美元(±2%),成为首家单季营收突破千亿美元的半导体公司。按此年化营收 4320 亿美元,NVIDIA 已跃居全球第六大公司。同时,应收账款周转天数(DSO)从 45 天升至 60 天,反映其正为投资级客户提供更长的付款期限以支撑需求。
推荐理由:作者用 DSO 从 45 天跳到 60 天、应收账款增速远超营收等数据,提示 NVIDIA 增长背后对客户信用扩张的风险信号。
01:56
LMSYS:Blog(Chatbot Arena 团队)精选
Ling-3.0-flash 在 4 块 Blackwell GPU 上如何将批处理 1 解码延迟降低 54%蚂蚁 Ling Infra 团队与 RadixArk SGLang 团队将 Ling-3.0-flash 混合线性注意力 MoE 模型的单请求解码速度从 288 tok/s 提升至 606 tok/s,平均 TPOT 从 3.33 ms 降至 1.53 ms。
推荐理由:对做单请求低延迟推理的团队,可迁移的是先把主机从 GPU 进度上解绑再优化 GPU 关键路径,否则主机的同步等待会变成每步的 GPU 气泡,这与具体模型无关。
21:22
NVIDIA 与 SB Energy 合作锁定俄亥俄州 PORTS-Pike 园区电力容量,OpenAI 将入驻NVIDIA 宣布与 SB Energy 合作,锁定俄亥俄州 PORTS-Pike 科技园区的电力容量(LPS)以独家部署 NVIDIA 算力,OpenAI 将成为租户。
另有 5 家信源报道X:Rohan Paul (@rohanpaul_ai)OpenAI:官网动态(RSS · 排除企业/客户案例)The Decoder:AI News(RSS)X:Kim (@kimmonismus)IT之家(RSS)
推荐理由:NVIDIA以20年租约担保为OpenAI锁定4.25GW容量,把AI工厂重资产负担部分转由自己承担,使高增长实验室能越过资产负债表限制获取算力。
00:03
2026年夏季开源模型生态观察:中国前沿模型规模领先,AMD与NVIDIA主导发布量2026年1至8月,Hugging Face公开模型仓库从243万增至296万,但85.6%的模型下载量不足200次,1.5%的仓库占据99.2%下载量。中国实验室月度最大开源模型参数规模在754B至2.78万亿之间,美国实验室七个月中五个月低于130B。AMD与NVIDIA各发布超200个新模型仓库,成为发布开源模型最多的机构。
推荐理由:下载量与点赞量分别记录实际依赖和社区兴奋点,把两者混为同一个热度指标是评估开源模型生态时最常见的偏差。
23:11
消息称英伟达开发万亿参数开源 AI 模型 Nemotron 4,目标挑战全球顶级英伟达正在研发新一代开源 AI 模型系列 Nemotron 4,规模最大的模型预计至少拥有 1 万亿个参数,旨在与全球最先进的开源模型竞争。英伟达尚未确定发布日期,最终训练也未完成,员工认为该模型最早可能在今年秋末准备就绪。此举意在通过开放模型生态扩大 AI 应用范围,并推动市场对其 GPU 算力的需求。
另有 1 家信源报道The Decoder:AI News(RSS)
推荐理由:万亿参数开源模型的推进可能改变企业从选型到部署的决策链路,同时强化英伟达 GPU 在开源生态中的需求绑定。
21:51
LMSYS:Blog(Chatbot Arena 团队)精选
SGLang 宣布 Day-0 支持 NVIDIA Nemotron 3.5 LightningSGLang 宣布对 NVIDIA Nemotron 3.5 Lightning 提供 Day-0 支持,该开源模型为 30B 总参数、3B 激活参数的混合专家架构,支持最长 1M token 上下文,可从 Hugging Face 下载 BF16 和 NVFP4 权重。模型支持 MTP、DFlash、DSpark 三种投机解码技术,并可通过 OpenAI 兼容 API 接入智能体工作流。
推荐理由:Nemotron 3.5 Lightning以3B活跃参数提供前沿agent能力,SGLang的即刻支持让开发者能用单命令启动高性能推理,将agent部署成本大幅压缩。
21:13
NVIDIA 推出 Nemotron 3.5 Lightning,加速本地智能体任务NVIDIA 发布 Nemotron 3.5 Lightning,一款可定制的开源 30B 混合专家(MoE)模型,专为常驻智能体设计。相比同类开源模型,其 token 生成速度最高提升 4 倍,任务完成时间缩短 30%。该模型采用开放权重,支持用户微调以匹配特定任务,并可在 RTX PC、DGX Spark 及 Jetson 等设备上运行。
另有 8 家信源报道X:Testing Catalog (@testingcatalog)MarkTechPost(RSS)X:Artificial Analysis (@ArtificialAnlys)X:阿易 AI Notes (@AYi_AInotes)X:洪明 (@hongming731)The Decoder:AI News(RSS)IT之家(RSS)NVIDIA Blog(RSS)
推荐理由:本地运行 30B MoE 模型并声称 4 倍加速,与开源路由器结合可自动分配任务到最合适模型,给控制推理成本提供了新路径。
05:58
Jensen Huang@JensenHuang精选 英伟达联合六大机构融资5000亿美元建AI工厂http://x.com/i/article/2086933422921117696NVIDIA AI Factory Compute Is Becoming an Investable Asset ClassNVIDIA AI Factory Compute Is Becoming an Investable Asset ClassToday, we announced partnerships with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR to establish independent financing platforms designed to mobilize over $500 billion of third-party capital to support the buildout of AI infrastructure over time.This is a major milestone for NVIDIA and the AI industry. We have moved from an era in which companies bought chips and built data centers project by project to one in which AI factories can be financed as productive infrastructure — with repeatable platforms, long-term institutional capital and a diverse customer base that uses compute to create revenue.AI has reached an inflection point. It is moving from research into production. AI is creating real value, and the infrastructure behind it is becoming one of the world’s most productive assets. In AI, compute is revenue.A New Infrastructure AssetNVIDIA compute is not just a chip. It is a complete AI factory platform including accelerated computing, networking, systems software, AI frameworks and a global developer ecosystem.NVIDIA DSX AI factories can run the world’s broadest range of AI models, modalities and algorithms — language, vision, speech, biology, physical AI and robotics. One NVIDIA AI factory can serve many customers and many workloads. That makes it flexible and fungible.It is also built on a globally adopted architecture used across every major cloud, and by systems makers and enterprises around the world. When needs change, the factory can be used by another customer, another cloud or another operator. This broad ecosystem gives NVIDIA compute a deep market of potential users and offtakers, helping protect residual value.CUDA makes the factory better over time. Every generation of NVIDIA software improves the performance, efficiency and total cost of ownership of already- installed infrastructure. The hardware does not stand still: software innovation allows an AI factory to produce more intelligence at lower cost throughout its life, extending its useful economic value.NVIDIA A100 is a powerful example. NVIDIA introduced the Ampere-based A100 in 2020, and six years later, it remains in active commercial use for AI training, fine-tuning, inference and high-performance computing. Customers continue to commit capacity for multi-year deployments, extending A100’s economic life toward a decade.The market is also demonstrating the durability of NVIDIA compute economics. One-year H100 rental pricing rose from about $1.70 per GPU-hour in October 2025 to about $2.35 per GPU-hour in March 2026. Cross-provider on-demand median pricing rose from roughly $2.00 per GPU-hour in October 2025 to $2.70 in June 2026. Blackwell capacity commands a premium, with reported B200 cloud rates spanning approximately $5.30 to $7.05 per GPU-hour.That is what makes NVIDIA AI factories different. Their value is not fixed at installation: CUDA continuously improves their output; the installed base remains productive well beyond its initial depreciation period; and the same standard architecture serves a deep, growing global market of AI workloads.These are the characteristics of an investable infrastructure asset: it produces revenue, serves a broad market, improves in performance over time and can be redeployed.Bringing Capital to AI FactoriesThe demand for AI infrastructure is extraordinary. But access to capital is uneven. Many great AI companies, enterprises and AI clouds have demand for compute but do not yet have access to financing at the scale or cost required to build quickly.That is why we are partnering with the world’s leading long-term capital providers.Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR are also among the world’s leading infrastructure investors, with deep expertise in underwriting long-lived, productive assets. Together, we are creating repeatable financing platforms to help the AI ecosystem build the factories it needs.The platforms are designed to help qualified AI labs, enterprises and AI clouds access AI-factory infrastructure at scale. The more than $500 billion figure represents aggregate third-party capital that these platforms are designed to mobilize over time — the capital is not NVIDIA revenue, a single fund or a commitment to a single customer.The financial institutions will independently assess each opportunity — the customer, demand, utilization, cash flow and residual value. NVIDIA provides the AI factory platform. The financial institutions provide long-term capital and financing expertise.The Important QuestionsIs this circular financing?This initiative is designed to address that concern. We are bringing independent, long-term institutional capital into the AI infrastructure market.The demand is real: it comes from frontier AI labs, AI-native startups, enterprises, cloud providers and countries building AI services. The capital providers independently underwrite each project — including the customer, demand, utilization, cash flow and residual value. NVIDIA provides the platform; the investors make independent financing decisions.This is the beginning of an open capital market for AI infrastructure.Why would NVIDIA support financing?In some cases, NVIDIA may provide a residual-value support mechanism for up to 25% of an opportunity, assessed carefully on a project-by-project basis. That support is limited, residual-value based and designed to complement — not replace — independent underwriting.This is substantially lower than other compute-financing arrangements. NVIDIA can provide support because NVIDIA compute is unique: it is fungible, universally adopted, software-upgradable and redeployable across a large ecosystem of customers.Our role is to help unlock a very large pool of independent capital while maintaining disciplined risk exposure.Can the market absorb this capacity?The question is not whether we are building data centers. The question is whether we are building productive AI factories.An AI factory turns energy and data into valuable intelligence. Its customers are broad: frontier AI labs, AI clouds, enterprises and nations. They are building AI because it has become useful — doing valuable work across every industry.There is discipline in the model. Each financing partner will independently evaluate demand, utilization, cash flow and residual value. Capacity will be built around real customer economics.Where is the return on investment?The return is in the usefulness of AI.Companies are using AI to write software, discover drugs, design products, serve customers, automate operations and build new services. AI factories make this possible. More compute creates better AI; better AI creates more usage; more usage creates more revenue; and more revenue drives more compute.This is the virtuous cycle of the AI industrial revolution.The Infrastructure of IntelligenceEvery industrial revolution has been built on infrastructure: electricity, transportation, communications and computing, with every buildout enabled by external financing.AI factories are the infrastructure of the intelligence era.With these partnerships, NVIDIA and the world’s leading financial institutions are creating a new way to finance the infrastructure that will power this industrial revolution. We will make AI factories more accessible to the companies, industries and nations building the future.The age of AI is here. Together, we will build the infrastructure to power it.译英伟达宣布与Apollo、BlackRock、Blackstone、Brookfield、Goldman Sachs和KKR合作,建立独立融资平台,动员超5000亿美元第三方资本支持AI基础设施建设。另有 5 家信源报道NVIDIA Blog(RSS)X:Rohan Paul (@rohanpaul_ai)IT之家(RSS)The Decoder:AI News(RSS)X:阑夕 (@foxshuo)
推荐理由:NVIDIA 将 AI 工厂论证为可复用的生产性资产,并联合大型金融机构建立融资平台,这对 AI 基础设施从项目融资转向长期资本市场的进程可能产生影响。
21:12
NVIDIA Cosmos 3:开放世界模型如何推动物理 AI 前沿NVIDIA 发布 Cosmos 3,一个基于混合 Transformer 架构的开放物理 AI 基础全模态模型,整合视觉推理、世界生成与动作预测。
推荐理由:介绍 Cosmos 3 作为开放物理 AI 基础模型的架构、基准表现和行业用例,为需要定制世界模型的机器人、自动驾驶和视觉 AI 团队提供了可直接参考的技术路线。