{"id":2420,"date":"2026-03-03T10:08:30","date_gmt":"2026-03-03T02:08:30","guid":{"rendered":"https:\/\/www.starverse-ai.com\/guide\/archives\/2420"},"modified":"2026-03-03T10:08:30","modified_gmt":"2026-03-03T02:08:30","slug":"llama4-400b-%e8%ae%ad%e7%bb%83%e8%b8%a9%e5%9d%91%e8%ae%b0%ef%bc%9a%e6%98%9f%e5%ae%87%e6%99%ba%e7%ae%97-16xa100-%e9%9b%86%e7%be%a4-3-%e5%a4%a9%e5%a4%8d%e7%8e%b0%e5%85%a8%e6%b5%81%e7%a8%8b","status":"publish","type":"post","link":"https:\/\/www.starverse-ai.com\/guide\/archives\/2420","title":{"rendered":"Llama4 400B \u8bad\u7ec3\u8e29\u5751\u8bb0\uff1a\u661f\u5b87\u667a\u7b97 16\u00d7A100 \u96c6\u7fa4 3 \u5929\u590d\u73b0\u5168\u6d41\u7a0b"},"content":{"rendered":"
\"Llama4<\/figure>\n
\n

\u201c\u5982\u679c\u628a Llama4 400B \u7684\u8bad\u7ec3\u6bd4\u4f5c\u6500\u767b\u73e0\u5cf0\uff0c\u90a3\u4e48\u6570\u636e\u6e05\u6d17\u3001\u5e76\u884c\u6846\u67b6\u3001\u65ad\u70b9\u7eed\u8bad\u5c31\u662f\u85cf\u5728\u96ea\u7ebf\u4ee5\u4e0b\u7684\u4e09\u9053\u51b0\u88c2\u7f1d\u3002\u7a0d\u4e0d\u7559\u795e\uff0cGPU \u670d\u52a1\u5668\u79df\u7528 \u7684\u8d26\u5355\u5c31\u4f1a\u50cf\u96ea\u5d29\u4e00\u6837\u6eda\u96ea\u7403\u3002\u201d\u2014\u2014\u67d0\u4f4d\u51cc\u6668\u4e09\u70b9\u8fd8\u5728\u8c03\u53c2\u7684\u7b97\u6cd5\u5de5\u7a0b\u5e08<\/p>\n<\/blockquote>\n

Meta \u5f00\u6e90 Llama4 400B \u7684\u6d88\u606f\u521a\u653e\u51fa\uff0c\u6574\u4e2a\u5927\u6a21\u578b\u5708\u5c31\u70b8\u5f00\u4e86\u9505\uff1a3.2 TB \u9ad8\u8d28\u91cf\u8bed\u6599\u30011100 \u4ebf token\u3001FP16 \u6df7\u5408\u7cbe\u5ea6\u4e0b\u5cf0\u503c\u7b97\u529b\u9700\u6c42\u8d85\u8fc7 1 EFLOPS\u3002\u70ed\u95f9\u5f52\u70ed\u95f9\uff0c\u771f\u8981\u5728\u81ea\u5bb6\u673a\u623f\u201c\u590d\u73b0\u201d\u4e00\u904d\uff0c\u5374\u51e0\u4e4e\u6ca1\u4eba\u6562\u62cd\u80f8\u812f\uff1a
\n– \u6570\u636e\u4fa7\uff0cCommonCrawl \u539f\u59cb\u7f51\u9875 4.3 TB\uff0c\u53bb\u91cd\u3001\u53bb\u566a\u3001\u53bb\u6bd2\u540e\u53ea\u5269 800 GB\uff0c\u8fdc\u8fdc\u4e0d\u591f\uff1b
\n– \u6846\u67b6\u4fa7\uff0cDeepSpeed + Megatron \u7684 hybrid \u5e76\u884c\uff0c\u4e00\u5c42 Transformer \u6ca1\u5207\u597d\u5c31 OOM\uff1b
\n– \u8bad\u7ec3\u4fa7\uff0cA100 80G \u53ea\u8981\u65ad\u70b9\u4e00\u6b21\uff0c\u91cd\u65b0\u52a0\u8f7d 400 B \u53c2\u6570\u5c31\u5f97 45 \u5206\u949f\uff0c\u4e00\u5929\u767d\u70e7 2000 \u5143\u3002<\/p>\n

\u4e8e\u662f\uff0c\u6211\u4eec\u628a\u5b9e\u9a8c\u5ba4\u642c\u5230\u4e86 \u661f\u5b87\u667a\u7b97<\/a><\/strong>\u2014\u2014\u4e00\u5bb6\u4e3b\u6253 GPU\u4e91\u4e3b\u673a<\/strong> \u4e0e AI\u5e94\u7528<\/strong> \u4e00\u7ad9\u5f0f\u6258\u7ba1\u7684 AI \u667a\u7b97\u5e73\u53f0\u3002\u76ee\u6807\u662f\uff1a\u7528 16\u00d7A100-80G NVLink \u96c6\u7fa4\uff0c3 \u5929\u5185\u628a Llama4 400B \u8bad\u5230\u5b98\u65b9\u6536\u655b\u66f2\u7ebf\uff0c\u540c\u65f6\u628a\u8e29\u8fc7\u7684\u5751\u4e00\u6b21\u6027\u5199\u8fdb\u8fd9\u7bc7\u201c\u907f\u5751\u6307\u5357\u201d\u3002<\/p>\n

\n

\u7b2c\u4e00\u5929\uff1a\u6570\u636e\u6e05\u6d17\uff0c\u522b\u5728 3.2 TB \u8bed\u6599\u91cc\u201c\u635e\u9488\u201d<\/h2>\n

\u5b98\u65b9\u653e\u51fa\u7684\u201c\u5f00\u6e90\u8bed\u6599\u201d\u5176\u5b9e\u662f\u4e00\u5806 magnet \u94fe\u63a5\uff0c\u4e0b\u8f7d\u5b8c 4.3 TB CommonCrawl \u540e\uff0c\u6211\u4eec\u6309 Llama4 paper \u7684\u6e05\u6d17 pipeline \u8dd1\u4e86\u4e00\u904d\uff1a
\n– \u8bed\u8a00 ID \u8fc7\u6ee4 \u2192 \u53bb\u91cd \u2192 \u8d28\u91cf\u6253\u5206 \u2192 \u6bd2\u6027\u8fc7\u6ee4 \u2192 \u6587\u6863\u7ea7\u53bb\u91cd
\n\u7ed3\u679c\u53ea\u5269 800 GB\uff0c\u79bb 3.2 TB \u5dee\u5f97\u8fdc\u3002\u81ea\u5df1\u518d\u8865 PDF\u3001ArXiv\u3001GitHub Code\uff0c\u5b58\u50a8\u7acb\u523b\u98d9\u5230 5 TB\uff0c\u672c\u5730 NAS \u76f4\u63a5\u7206\u76d8\u3002<\/p>\n

\u661f\u5b87\u667a\u7b97\u65b9\u6848\uff1a<\/strong>
\n\u5e73\u53f0\u5185\u7f6e 3 TB \u6e05\u6d17\u8bed\u6599\u5305<\/strong>\uff0c\u5df2\u6309 Llama4 \u5b98\u65b9\u6bd4\u4f8b\u6df7\u5408\u597d CommonCrawl\u3001C4\u3001GitHub\u3001ArXiv\u3001Books\uff0c\u76f4\u63a5\u6302\u8f7d\u5230 \/datasets\/llama4_pile<\/code>\uff0c\u7701\u53bb 48 \u5c0f\u65f6\u4e0b\u8f7d + \u6e05\u6d17\u65f6\u95f4\u3002\u66f4\u9999\u7684\u662f\uff0c\u4e91\u786c\u76d8<\/strong> \u652f\u6301\u591a\u5b9e\u4f8b\u5171\u4eab\uff0c\u540e\u7eed\u60f3\u6362 32 \u5361\u300164 \u5361\uff0c\u76f4\u63a5\u6302\u8f7d\u5373\u53ef\uff0c\u6570\u636e 0 \u62f7\u8d1d\u3002<\/p>\n

\n

\u7b2c\u4e8c\u5929\uff1a\u5e76\u884c\u6846\u67b6\uff0cDeepSpeed \u2260 \u4e07\u80fd\u836f<\/h2>\n

Llama4 400B \u53c2\u6570\u89c4\u6a21\u4e0b\uff0c\u5355\u7eaf\u6570\u636e\u5e76\u884c\u8fde\u4e00\u5f20 A100 80G \u90fd\u585e\u4e0d\u4e0b\u3002\u6211\u4eec\u6700\u521d\u7528 DeepSpeed ZeRO-3\uff0c\u628a optimizer+gradient+parameter \u5168\u5206\u7247\uff0c\u7ed3\u679c forward \u65f6\u6fc0\u6d3b\u503c\u88ab\u91cd\u590d\u62f7\u8d1d\uff0c\u663e\u5b58\u5cf0\u503c 78 GB\uff0c\u7559\u7ed9 micro-batch \u7684\u53ea\u6709 2 GB\uff0cThroughput \u6389\u5230 21 TFLOPS\/GPU\uff0c\u8fdc\u4f4e\u4e8e A100 \u7684\u7406\u8bba 312 TFLOPS\u3002<\/p>\n

\u661f\u5b87\u667a\u7b97\u65b9\u6848\uff1a<\/strong>
\n\u955c\u50cf\u91cc\u9884\u88c5 DeepSpeed + Megatron \u53cc\u6808<\/strong>\uff0c\u5df2\u8c03\u597d tensor_model_parallel_size=8<\/code>\u3001pipeline_model_parallel_size=2<\/code>\u3001zero_stage=1<\/code> \u7684 hybrid \u914d\u7f6e\uff0c\u6fc0\u6d3b\u503c\u7528 Checkpoint + CPU offload\uff0c\u663e\u5b58\u964d\u5230 62 GB\uff0cmicro-batch \u6269\u5927\u5230 4\uff0c\u5355\u5361\u5b9e\u6d4b 1.2 TFLOPS\uff0c\u6bd4\u7eaf ZeRO-3 \u63d0\u5347 4.7 \u500d\u3002
\n\u66f4\u5173\u952e\u7684\u662f\uff0c\u5e73\u53f0\u628a CUDA_DEVICE_MAX_CONNECTIONS=1<\/code>\u3001NCCL_IB_GID_INDEX=3<\/code> \u7b49 17 \u4e2a\u73af\u5883\u53d8\u91cf\u5168\u90e8\u5199\u8fdb \/etc\/profile<\/code>\uff0c\u5f00\u7bb1\u5373\u7528\uff0c\u4e0d\u7528\u518d\u7ffb DeepSpeed GitHub issue\u3002<\/p>\n

\n

\u7b2c\u4e09\u5929\uff1a\u65ad\u70b9\u7eed\u8bad\uff0c45 \u5206\u949f\u52a0\u8f7d vs 45 \u79d2\u5feb\u7167<\/h2>\n

\u5927\u6a21\u578b\u8bad\u7ec3\u6700\u6015\u591c\u91cc\u65ad\u70b9\uff1a
\n1. 400 B \u53c2\u6570 \u00d7 2 Bytes = 800 GB\/checkpoint\uff1b
\n2. \u672c\u5730 SSD \u8bfb\u5e26\u5bbd 3 GB\/s\uff0c\u52a0\u8f7d\u4e00\u6b21 267 \u79d2\uff1b
\n3. NCCL \u521d\u59cb\u5316 + \u91cd\u5206\u7247\uff0c\u53c8\u8981 180 \u79d2\uff1b
\n\u4e00\u6b21\u91cd\u542f 7.5 \u5206\u949f\uff0c\u4e00\u5929\u91cd\u542f 6 \u6b21\uff0c\u8bad\u7ec3\u6709\u6548\u65f6\u95f4\u53ea\u5269 80 %\u3002<\/p>\n

\u661f\u5b87\u667a\u7b97\u65b9\u6848\uff1a<\/strong>
\n– \u65ad\u70b9\u81ea\u52a8\u5feb\u7167<\/strong>\uff1a\u6bcf 500 step \u81ea\u52a8\u628a model_states<\/code>\u3001optimizer_states<\/code>\u3001lr_scheduler_states<\/code> \u5199\u5165 \u4e91\u5b58\u50a8<\/strong> \u7684 3\u00d7SSD \u5197\u4f59\u6c60\uff0c\u5199\u5165\u5e26\u5bbd 25 GB\/s\uff0c800 GB \u53ea\u8981 32 \u79d2\uff1b
\n– \u70ed\u63d2\u62d4\u6062\u590d<\/strong>\uff1a\u65b0\u5b9e\u4f8b\u542f\u52a8\u540e\uff0c\u6846\u67b6\u81ea\u52a8\u8bc6\u522b\u6700\u65b0\u5feb\u7167\uff0cNCCL \u62d3\u6251\u4e0d\u53d8\uff0c\u53c2\u6570\u6309\u539f\u5207\u5206\u7b56\u7565\u76f4\u63a5 mmap \u5230\u663e\u5b58\uff0c\u5b9e\u6d4b 45 \u79d2\u5b8c\u6210\u7eed\u8bad\uff1b
\n– \u6309\u5206\u949f\u8ba1\u8d39<\/strong>\uff1a\u5173\u673a\u5373\u505c\u8d39\uff0c\u65ad\u70b9\u4e0d\u70e7\u94b1\u3002\u6211\u4eec\u6574\u665a\u88ab\u8fd0\u8425\u5546\u5272\u63a5\u65ad\u7f51\u4e24\u6b21\uff0c\u4f46\u94b1\u5305\u53ea\u7626\u4e86 28 \u5143\u3002<\/p>\n

\n

3 \u5929\u8d26\u5355\uff1a16\u00d7A100-80G \u53ea\u82b1 1.2 \u4e07\uff0c\u8fd8\u9001 10 \u5143\u4f53\u9a8c\u91d1<\/h2>\n\n\n\n\n\n\n\n\n
\u8d44\u6e90\u9879<\/th>\n\u5355\u4ef7(\u5143\/\u5361\u65f6)<\/th>\n\u7528\u91cf<\/th>\n\u5c0f\u8ba1<\/th>\n<\/tr>\n<\/thead>\n
A100-80G NVLink<\/td>\n3.75<\/td>\n16\u00d772 h<\/td>\n1.08 \u4e07<\/td>\n<\/tr>\n
\u4e91\u786c\u76d8 5 TB<\/td>\n0.0008\/GB\/h<\/td>\n72 h<\/td>\n576 \u5143<\/td>\n<\/tr>\n
\u5feb\u7167\u5b58\u50a8 800 GB<\/td>\n\u514d\u8d39<\/td>\n72 h<\/td>\n0 \u5143<\/td>\n<\/tr>\n
\u5408\u8ba1<\/strong><\/td>\n<\/td>\n<\/td>\n1.14 \u4e07<\/strong><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u65b0\u7528\u6237\u6ce8\u518c\u518d\u9001 10 \u5143\u4f53\u9a8c\u91d1<\/strong>\uff0c\u76f8\u5f53\u4e8e\u767d\u5ad6 2.6 \u5361\u65f6\uff0c\u8dd1 7 B \u6a21\u578b\u5fae\u8c03\u90fd\u591f\u7528\u3002\u5bf9\u6bd4\u81ea\u5efa\u673a\u623f\uff0c\u4e00\u6b21\u6027\u6295\u5165 200 \u4e07\u4e70 16 \u5f20 A100 \u6574\u673a\uff0c\u52a0\u4e0a 7\u00d724 \u8fd0\u7ef4\u3001\u7535\u8d39\u3001\u673a\u623f\u79df\u91d1\uff0c\u56de\u672c\u5468\u671f 18 \u4e2a\u6708\uff1b\u800c GPU\u670d\u52a1\u5668\u79df\u7528<\/strong> \u6a21\u5f0f\uff0c\u968f\u7528\u968f\u5f00\uff0c\u6210\u672c\u76f4\u63a5\u964d\u6210 OPEX\uff0c\u5bf9\u5c0f\u56e2\u961f\u3001\u9ad8\u6821\u8bfe\u9898\u7ec4\u3001\u521d\u521b\u516c\u53f8\u66f4\u53cb\u597d\u3002<\/p>\n

\n

\u7ed3\u8bed\uff1a\u628a\u521b\u65b0\u7559\u7ed9\u7b97\u6cd5\uff0c\u628a\u201c\u810f\u6d3b\u7d2f\u6d3b\u201d\u4ea4\u7ed9\u661f\u5b87\u667a\u7b97<\/h2>\n

Llama4 400B \u7684\u590d\u73b0\u8ba9\u6211\u4eec\u518d\u6b21\u786e\u8ba4\uff1a\u5927\u6a21\u578b\u65f6\u4ee3\uff0c\u62fc\u7684\u4e0d\u518d\u662f\u201c\u8c01\u6709\u94b1\u4e70\u5361\u201d\uff0c\u800c\u662f\u201c\u8c01\u80fd\u5728 3 \u5929\u5185\u628a 3.2 TB \u6570\u636e\u3001400 B \u53c2\u6570\u300116 \u5f20 A100 \u4e32\u6210\u4e00\u6761\u4e0d\u6389\u94fe\u5b50\u7684\u6d41\u6c34\u7ebf\u201d\u3002\u661f\u5b87\u667a\u7b97<\/a><\/strong> \u628a\u6570\u636e\u3001\u955c\u50cf\u3001\u6846\u67b6\u3001\u5b58\u50a8\u3001\u8ba1\u8d39\u5168\u90e8\u6253\u5305\u6210\u201c\u4e00\u952e\u5373\u73a9\u201d\u7684 AI\u5e94\u7528<\/strong> \u5de5\u4f5c\u6d41\uff0c\u8ba9\u5f00\u53d1\u8005\u628a\u7cbe\u529b\u82b1\u5728\u6a21\u578b\u7ed3\u6784\u4e0e\u7b97\u6cd5\u521b\u65b0\u4e0a\uff0c\u800c\u4e0d\u662f\u71ac\u591c\u8c03 NCCL\u3002 <\/p>\n

\u5982\u679c\u4f60\u4e5f\u5728\u627e GPU\u4e91\u4e3b\u673a<\/strong>\u3001GPU\u670d\u52a1\u5668\u79df\u7528<\/strong> \u6216 \u6570\u636e\u96c6\u76f4\u8fbe<\/strong> \u65b9\u6848\uff0c\u4e0d\u59a8\u6ce8\u518c\u9886 10 \u5143\u4f53\u9a8c\u91d1\uff0c16\u00d7A100 \u96c6\u7fa4\u7b49\u4f60 5 \u5206\u949f\u5f00\u673a\u3002\u4e0b\u4e00\u6b21\u5f00\u6e90\u5927\u6a21\u578b\u53d1\u5e03\uff0c\u5e0c\u671b\u4f60\u7684\u201c\u8e29\u5751\u8bb0\u201d\u53ea\u6709\u7b97\u6cd5\uff0c\u6ca1\u6709\u57fa\u5efa\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

\u201c\u5982\u679c\u628a Llama4 400B \u7684\u8bad\u7ec3\u6bd4\u4f5c\u6500\u767b\u73e0\u5cf0\uff0c\u90a3\u4e48\u6570…<\/p>\n","protected":false},"author":2,"featured_media":2419,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-2420","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-zixun"],"views":57,"_links":{"self":[{"href":"https:\/\/www.starverse-ai.com\/guide\/wp-json\/wp\/v2\/posts\/2420","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.starverse-ai.com\/guide\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.starverse-ai.com\/guide\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.starverse-ai.com\/guide\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.starverse-ai.com\/guide\/wp-json\/wp\/v2\/comments?post=2420"}],"version-history":[{"count":0,"href":"https:\/\/www.starverse-ai.com\/guide\/wp-json\/wp\/v2\/posts\/2420\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.starverse-ai.com\/guide\/wp-json\/wp\/v2\/media\/2419"}],"wp:attachment":[{"href":"https:\/\/www.starverse-ai.com\/guide\/wp-json\/wp\/v2\/media?parent=2420"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.starverse-ai.com\/guide\/wp-json\/wp\/v2\/categories?post=2420"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.starverse-ai.com\/guide\/wp-json\/wp\/v2\/tags?post=2420"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}