最新英伟达经济学：每美元性能是AMD的15倍，“买越多省越多”是真的

量子位01-01

梦晨发自凹非寺量子位 | 公众号 QbitAI为什么AI算力霸主永远是英伟达？不算不知道，一算吓一跳：在英伟达平台每花一美元，获得的性能是AMD的15倍。尽管英伟达卖的更贵，但只要买齐一套，就更省钱。来自Signal65的一份最新详尽报告揭示了这个现实，一定条件下生成同样数量的token，英伟达的成本只有AMD的十五分之一。这份报告基于SemiAnalysis Inference MAX的公开...

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QbitAI为什么AI算力霸主永远是英伟达？不算不知道，一算吓一跳：在英伟达平台每花一美元，获得的性能是AMD的15倍。尽管英伟达卖的更贵，但只要买齐一套，就更省钱。来自Signal65的一份最新详尽报告揭示了这个现实，一定条件下生成同样数量的token，英伟达的成本只有AMD的十五分之一。这份报告基于SemiAnalysis Inference MAX的公开...</p>\n\n<a href=\"https://tech.ifeng.com/c/8pYcg7KXW3s\">网页链接</a>\n\n</div>\n\n\n</article>\n</div>\n</body>\n</html>\n","isBrief":false,"type":0,"news_type":1,"symbol":"SG9999014914.USD","symbol_name":"UNITED GLOBAL QUALITY GROWTH (USDHDG) INC","start_time":0,"source_url":"https://tech.ifeng.com/c/8pYcg7KXW3s","article_id":"2600085758","we_media_id":null,"thumbnails":[],"rights":null,"url":"https://stock-news.laohu8.com/highlight/detail?id=2600085758","pubTimestamp":1767243148,"columns":[],"sourceInfo":{"source_id":"fenghuang_stock","name":"凤凰网"},"weMediaInfo":null,"summary":"不算不知道，一算吓一跳：在英伟达平台每花一美元，获得的性能是AMD的15倍。黄仁勋的“买的越多，省的越多”原来是真的。报告指出，无论是英伟达B200还是AMD MI355X，所有8卡系统在超出单节点规模后都会撞上“扩展天花板”。在75 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save\" is true","themeId":null,"isJumpTheme":false,"ttsUrl":null,"symbols_score_info":{"NVD3.UK":0.6,"NVDX":0.6,"3NVD.UK":0.6,"NVDD":0.6,"AMD":1.95,"NVD2.UK":0.6,"NVDS.UK":0.6,"NVD":0.6,"NVDS":0.6,"NVDA":1.98,"2NVD.UK":0.6,"NVDU":0.6,"SNVD.UK":0.6,"NVIW.SI":0.6,"NVDY":0.6},"content_text":"梦晨 发自 凹非寺量子位 | 公众号 QbitAI为什么AI算力霸主永远是英伟达？不算不知道，一算吓一跳：在英伟达平台每花一美元，获得的性能是AMD的15倍。尽管英伟达卖的更贵，但只要买齐一套，就更省钱。来自Signal65的一份最新详尽报告揭示了这个现实，一定条件下生成同样数量的token，英伟达的成本只有AMD的十五分之一。这份报告基于SemiAnalysis Inference MAX的公开基准测试数据，时间跨度从2025年10月到12月，覆盖了从密集模型到前沿MoE推理模型的全场景测试。黄仁勋的“买的越多，省的越多”原来是真的。MoE时代：8卡系统撞上Scaling天花板AI模型正在经历一场架构革命，打开Artificial Analysis排行榜就会发现，智能度排名前十的开源模型清一色都是MoE（Mixture of Experts，专家混合）推理模型。另一项来自OpenRouter的数据显示，超过50%的token流量正在被路由到推理模型上。MoE架构的核心思路是把模型参数拆分成多个专门化的“专家”子网络，每个token只激活其中一小部分。以经典的DeepSeek-R1为例，它拥有6710亿总参数，但每个token只激活370亿——这让它能以更低的计算成本提供前沿级别的智能。问题随之而来。当专家分布在多块GPU上时，GPU之间的通信延迟会导致计算单元空闲等待数据，这些空闲时间直接转化为服务商的成本。报告指出，无论是英伟达B200还是AMD MI355X，所有8卡系统在超出单节点规模后都会撞上“扩展天花板”（scaling ceiling）。英伟达GB200 NVL72的解法是把72块GPU通过NVLink连接成一个单一域，提供130 TB/s的互联带宽。在软件层面，整个系统就像一块巨型GPU一样运作。配合英伟达Dynamo推理框架的分离式预填充-解码调度和动态KV缓存路由，这套架构能够有效突破8卡系统的通信瓶颈。模型越复杂，英伟达的优势越明显报告测试了三类典型模型：模型越复杂，英伟达的优势越明显。在密集模型Llama 3.3 70B上，英伟达B200对比AMD MI355X的领先幅度相对温和。在基线交互性（30 tokens/sec/user）下，B200的性能约为MI355X的1.8倍；当交互性要求提升到110 tokens/sec/user时，这一差距扩大到6倍以上。中等规模的MoE模型GPT-OSS-120B开始让差距变得更加显著。这款OpenAI开源模型拥有1170亿总参数，但每个token只激活约51亿参数。在2025年12月的测试数据中，100 tokens/sec/user交互性下B200的性能接近MI355X的3倍。在更符合推理模型需求的250 tokens/sec/user条件下，差距扩大到6.6倍。两个平台的绝对性能相比10月都有显著提升，英伟达的峰值吞吐从约7000 tokens/sec跃升至14000以上，AMD则从约6000提升到8500左右，但相对差距反而拉大了。真正的分水岭出现在前沿推理模型DeepSeek-R1上。这款模型集MoE路由、大参数规模和高强度推理生成于一身，对基础设施的要求极为苛刻。测试结果显示：在25 tokens/sec/user交互性下，GB200 NVL72的每GPU性能是H200的10倍、MI325X的16倍；在60 tokens/sec/user下，相比H200的优势扩大到24倍，相比MI355X达到11.5倍；在75 tokens/sec/user下，GB200 NVL72的性能是B200单节点配置的6.5倍，是MI355X的28倍。更关键的是，GB200 NVL72能够达到竞争平台根本无法企及的水平，在28卡配置下可以输出超过275 tokens/sec/user，而MI355X在相当吞吐水平下的峰值只有75 tokens/sec/user。Token经济学：贵了1.86倍，便宜了15倍直觉上，性能更强的平台应该更贵。事实也确实如此：根据Oracle Cloud的公开定价，GB200 NVL72的每GPU每小时价格为16美元，MI355X为8.60美元，前者是后者的1.86倍。如果参照CoreWeave的定价，GB200 NVL72相比上一代H200的价格也贵了约1.67倍。但报告的计算揭示了一个反直觉的结论：在25 tokens/sec/user交互性下，GB200 NVL72的性能优势为5.85倍，除以1.86倍的价格溢价，每美元性能仍是MI355X的3.1倍。在75 tokens/sec/user交互性下，28倍的性能优势除以1.86倍的价格，每美元性能达到MI355X的15倍，这意味着生成同等数量的token，英伟达平台的成本只有AMD的十五分之一。与上一代产品的对比同样惊人。报告估算在DeepSeek-R1的典型工作负载下，GB200 NVL72相比H200的性能提升约20倍。而GB200 NVL72价格仅上涨1.67倍，换算下来每美元性能提升约12倍，单token成本降至H200的十二分之一。MoE推理让网络成为推理成本的瓶颈，而机柜级的GB200 NVL72恰好解决了这个问题。价值的衡量标准正在从单纯的算力转向“每美元能产出多少智能”。报告在结论中指出，AMD的竞争力并未被完全否定——在密集模型和容量驱动的场景下，MI325X和MI355X仍有用武之地。AMD的机柜级解决方案Helios也在开发中，可能在未来12个月内缩小差距。但就当前的前沿推理模型而言，从芯片到互联到软件的端到端平台设计，已经成为成本效益的决定性因素。","kind":"news","is_publish_news":true,"is_publish_highlight":false,"is_publish_live":false,"is_publish_wemedia":null,"editions":null,"column":"","sentiment":"2","news_tag":"dataReport","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":1,"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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