盛会直击：英伟达GTC大会四大核心重磅发布

每日经济新闻03-23

英伟达在GPU领域深耕多年，自1999年发布首款GPU至今，已有约27年时间。其芯片制程从220纳米迭代至4纳米左右，未来还将向1.6纳米推进，这也是我们较为期待的投资价值。　　本轮AI浪潮始于2023年，当时市场主流GPU为A100与H100。截至目前，市场主流GPU已更新为Blackwell架构芯片。A100与H100的核心技术特征是什么？其中H100芯片性能强劲，在2023年AI浪潮爆发后，...

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Release","themeId":null,"isJumpTheme":false,"ttsUrl":null,"symbols_score_info":{"NVDS":0.6,"NVDS.UK":0.6,"2NVD.UK":0.6,"NVII":0.6,"NVDY":0.6,"NVDA":1.98,"NVDX":0.6,"NVDU":0.6,"NVIW.SI":0.6,"NVD2.UK":0.6,"DIPS":0.6,"SNVD.UK":0.6,"ANV":0.6,"NVD":0.6,"NVD3.UK":0.6,"NVDQ":0.6,"NVDW":0.6,"NVDG":0.6,"3NVD.UK":0.6,"NVDD":0.6,"NVDB":0.6},"content_text":"英伟达在GPU领域深耕多年，自1999年发布首款GPU至今，已有约27年时间。其芯片制程从220纳米迭代至4纳米左右，未来还将向1.6纳米推进，这也是我们较为期待的投资价值。　　本轮AI浪潮始于2023年，当时市场主流GPU为A100与H100。截至目前，市场主流GPU已更新为Blackwell架构芯片。A100与H100的核心技术特征是什么？其中H100芯片性能强劲，在2023年AI浪潮爆发后，迅速成为市场炙手可热的GPU产品。　　H100由中国台湾台积电采用4纳米工艺代工生产，单芯片集成800亿个晶体管，还专门内置了Transformer模型引擎。为什么要专门针对Transformer做硬件适配？当前国内外我们耳熟能详的各类大模型，其底层架构基本都是基于Transformer基础架构针对性优化发展而来。　　英伟达极具前瞻性地在Hopper架构中，从硬件层面对Transformer做了专项优化，也就是引入了对应的专用引擎。英伟达也凭借这一核心优势，在短短两年多的时间里，从一家规模中等的企业，成长为全球市值第一的科技巨头，由此也能充分看到AI产业的强劲爆发力。　　总结来看，H100的核心优势是极致适配大模型训练场景。其在大模型训练中的能效比与运算速度，相比上一代Ampere架构的A100芯片，均有大幅提升。　　英伟达在2023年前后发布的Blackwell芯片，则重点针对AI推理场景做了优化。AI产业发展的后半程，推理场景的重要性愈发凸显，Blackwell在推理场景优化上做了大量技术布局，其中就包括低精度训练技术。什么是低精度运算？GPU的晶体管数量是有限的，采用低精度数据进行计算，能够在单位时间内完成更多次运算，完美契合推理场景降本提效的核心需求。因此Blackwell架构更侧重推理场景优化，同时也保留了极强的训练性能。　　展望未来，大家可以重点关注英伟达的产品发布节奏与技术路线图。本届GTC大会上，英伟达正式披露了其未来数年的新品规划，清晰梳理并展望了从Blackwell到Rubin再到Feynman的技术演进路线。其中2027年将推出RubinUltra架构、CPX芯片与CPU交换机，2028年将实现Feynman架构芯片的量产，这些重磅新品均已在本届GTC大会上完成首次亮相。　　本届大会的核心重磅发布之一，就是Feynman架构芯片。目前英伟达市场主流GPU仍是Blackwell架构的GB300，该芯片已实现量产，目前仍在持续放量的GPU产品均基于GB300基础架构。按照本次大会披露的规划，英伟达将于今年下半年推出Rubin架构的新一代GPU，2028年实现Feynman架构芯片的量产，整体架构迭代速度非常快。　　。本次大会官方披露，Feynman架构芯片将采用台积电A16制程生产，是全球首款1.6nmAI芯片。相较台积电上一代N2P工艺，A16制程在同等电压下速度提升约10%，更关键的是晶体管密度提高1.1倍，实现了翻倍以上的增长。摩尔定律带来的计算密度提升，让Feynman架构芯片的性能表现实现了重大突破，单GPU算力达50PFLOPS，推理性能是Blackwell的5倍。该芯片的核心升级集中在制程层面，同时采用背部供电技术，面向机器人、世界模型场景打造，初期英伟达将独享A16产能，2028年正式量产。　　第二个核心重磅发布，是大算力机柜的升级。当前AI机柜的算力规模持续扩大，英伟达目前主流的Blackwell架构GB300机柜，单机柜搭载72张GPU卡。本届大会上，英伟达正式推出了单机柜576卡的Rubin Ultra NVL576卡机柜，完整披露了这款机柜的核心架构设计。本次大会正式展出了Rubin Ultra所采用的Kyber机架与正交背板连接方案，该方案采用正交背板实现前后向连接，将垂直部署的计算刀片与交换刀片直连，在Scale up架构中替代传统铜缆，有效提升单机柜算力集成度，相关光互联技术细节也同步在本届GTC大会上完成披露，成为本次大会的核心亮点之一。　　第三个核心发布内容，是英伟达CPO交换机的技术进展与产业规划。CPO是一项新兴技术，也是英伟达重点推进的技术方向。本届GTC大会上，英伟达正式展示了SN6800、SN6810、Q3450三款量产落地的CPO交换机，这是全球首款量产的共封装光学交换机，标志着CPO技术正式从实验室走向规模化商用。同时英伟达对CPO技术做了全面的技术解读与产业规划披露，黄仁勋明确表示“需要更多的铜缆产能，更多的光芯片产能，更多的CPO产能”，确立了铜缆、光学、CPO三条路线并行推进的发展路径，CPO技术的渗透率提升速度与规模化放量进度有望持续超出市场预期。　　第四个核心发布内容，是市场高度关注的LPU芯片与配套机柜。英伟达去年已推出CPX系列产品，本届大会正式发布了整合Groq技术的LPU芯片与配套机架，之所以持续布局LPU芯片，核心原因是适配快速增长的AI推理场景需求。AI发展初期，尤其是2023年，绝大部分算力都用于大模型训练；但对于商业化云厂商而言，模型训练完成后，需要通过落地应用实现商业变现，不能只做投入。因此长期来看，AI推理赛道具备极高的产业价值。　　AI推理有明确的技术特征，整体分为Prefill（预填充）与Decode（解码）两个阶段。这两个阶段对硬件的需求存在差异，因此要提升推理效率，最优方案是将两个阶段拆分运行。英伟达为此做了大量技术布局，无论是去年发布的CPX芯片，还是本届GTC大会上正式发布的LPU芯片，核心目标都是更好地适配AI推理的发展趋势。该LPU芯片可视为专为推理场景设计的专用集成电路（ASIC），采用三星4nm制程，单芯片带宽达150TB/S，核心目标是实现极致的低延迟与高吞吐量，引入后将由其负责Decode环节，相较于BlackwellNVL72吞吐效率提升35倍，预期2026年下半年出货。英伟达在AI训练领域已建立极高的技术壁垒，其也希望在推理赛道构建起同样坚固的护城河。　　以上就是本届GTC大会我们重点梳理的核心发布内容，在此做简要总结：　　1.Rubin Ultra576卡机柜及其先进互联方案；　　2.Feynman架构芯片的正式亮相与参数披露；　　3.CPO交换机的量产落地与技术规划解读；　　4.LPU等推理专用芯片的正式发布与落地计划。　　长期来看，AI行业发展远未触顶，2026年全球AI资本开支预计超7000亿美元，Agent技术进入规模化商用元年，产业成长空间全面打开。投资层面，核心推荐通信ETF国泰（515880），光模块、服务器等算力核心权重超75%，2025年涨幅居全市场ETF首位；半导体设备ETF国泰（159516）受益于全球存储扩产与国产替代双重逻辑，具备充足业绩弹性。同时短期布局需警惕美联储货币政策不确定性、大宗商品涨价推高运营成本等潜在风险。（文章来源：每日经济新闻）","kind":"news","is_publish_news":true,"is_publish_highlight":false,"is_publish_live":false,"is_publish_wemedia":null,"editions":null,"column":"","sentiment":"1","news_tag":"productRelease","news_rank":0,"isVideo":false,"video":null,"symbols":[],"gpt_button":0,"need_auth":false,"need_login_tip":false,"code":"91000000","status":"200"},"commentList":[],"isCommentEnd":true,"newsSizeData":{"likeSize":0,"commentSize":0,"repostSize":0,"favoriteSize":0,"likeStatus":false,"favoriteStatus":false},"APP":{"userAgent":"Mozilla/5.0 AppleWebKit/537.36 (KHTML, like Gecko; compatible; ClaudeBot/1.0; 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