{"id":6581,"date":"2024-03-29T09:57:32","date_gmt":"2024-03-29T01:57:32","guid":{"rendered":"https:\/\/www.1ai.net\/?p=6581"},"modified":"2024-03-29T09:57:32","modified_gmt":"2024-03-29T01:57:32","slug":"%e8%8b%b1%e4%bc%9f%e8%be%be-ai%e8%8a%af%e7%89%87-h200-%e5%bc%80%e5%a7%8b%e4%be%9b%e8%b4%a7%ef%bc%8c%e6%80%a7%e8%83%bd%e7%9b%b8%e6%af%94-h100-%e6%8f%90%e5%8d%87-60-90","status":"publish","type":"post","link":"https:\/\/www.1ai.net\/en\/6581.html","title":{"rendered":"NVIDIA AI chip H200 starts shipping, performance improved by 60%-90% compared to H100"},"content":{"rendered":"

On March 28, according to the Nikkei report today,Nvidia<\/a>Cutting-edge image processing semiconductors (GPU<\/a>\uff09H200<\/a> Now available for delivery. H200 is a semiconductor for the AI field, with performance exceeding the current flagship H100.<\/p>\n

According to the performance evaluation results released by NVIDIA, taking the processing speed of Meta's large language model Llama 2 as an example,H200 Generative AI answers are processed up to 45% faster than H100<\/span>.<\/p>\n

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Market research firm Omdia has said that Nvidia will account for about 80% of the AI semiconductor market in 2022. AMD<\/a> Competitors such as Nvidia are also developing products to compete with Nvidia, and the competition is intensifying.<\/p>\n

NVIDIA announced at its developer conference on March 18 local time that it will launch a new generation of AI semiconductor "B200" this year. The new product, a combination of B200 and CPU (central processing unit), will be used on the latest LLM.The "most powerful AI accelerator card" GB200 includes two B200 Blackwell GPUs and an Arm-based Grace CPU\u00a0<\/span>The performance of reasoning large language models is 30 times higher than that of H100, and the cost and energy consumption are reduced to one 25th.<\/p>\n

GB200 adopts the new generation AI graphics processor architecture\u00a0Blackwell<\/span>, Huang Renxun said at the GTC conference: "Hopper is already very good, but we need a more powerful GPU."<\/p>\n

As previously reported, NVIDIA H200 was released in November last year. It is based on NVIDIA's "Hopper<\/span>The HGX H200 GPU, based on the company\u2019s NVIDIA NVIDIA GeForce architecture, is the successor to the H100 GPU and the company\u2019s first chip to use HBM3e memory, which is faster and has more capacity, making it better suited for large language models.Compared with the previous dominant H100, the performance of H200 has been directly improved from 60% to 90%.<\/span>Nvidia said: "With HBM3e, the Nvidia H200 delivers 141GB of memory at 4.8 TB per second, nearly twice the capacity and 2.4 times the bandwidth compared to the A100."<\/p>","protected":false},"excerpt":{"rendered":"

March 28, according to the Nihon Keizai Shimbun reported today, NVIDIA's cutting-edge image processing semiconductor (GPU) H200 is now available. H200 is a semiconductor for the field of AI, and its performance exceeds that of the current mainstay, H100. According to the performance evaluation results released by NVIDIA, for example, in the case of processing speed of Meta's large-language model, Llama 2, H200 has a maximum increase of 45% in processing speed over H100 for generative AI-derived answers. According to NVIDIA's performance evaluation results, for example, the processing speed of Meta's large language model Llama 2, the processing speed of H200 compared with that of H100, the processing speed of generative AI exported answers increased by up to 45%. Market research organization Omdia has said that in 2022 NVIDIA in the AI semiconductor market accounted for about 80% of the share, while at the same time AMD and other competitors are also developing products to fight against NVIDIA, the competition is intensifying. NVIDIA<\/p>","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[146],"tags":[1099,333,415,1938,239],"collection":[],"class_list":["post-6581","post","type-post","status-publish","format-standard","hentry","category-news","tag-ai","tag-amd","tag-gpu","tag-h200","tag-239"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/posts\/6581","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/comments?post=6581"}],"version-history":[{"count":0,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/posts\/6581\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/media?parent=6581"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/categories?post=6581"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/tags?post=6581"},{"taxonomy":"collection","embeddable":true,"href":"https:\/\/www.1ai.net\/en\/wp-json\/wp\/v2\/collection?post=6581"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}