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NVIDIAがAIおよびHPC向けGPU「H200」を発表、H100と比べて推論速度が2倍でHPC性能はx86 CPUの110倍 - GIGAZINE

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.donate_title { font-size: 1.6rem; color: #000; background: #fd0; line-height: 1.1em; font-weight: 700; word-wrap: break-word; padding: 2px 1px 2px 5px; margin: 0 0 6px 0; width: 100%; cursor: pointer; } .amount_options { max-width: 76vw; width: 100%; display: block; margin-top: 8px; grid-area: 1 / 1 / 1 / -1; } .amount_options ul { display: flex; margin: 0; padding: 0; list-style: none; } .amount_options li { flex: 1 0 0; padding: 4px; } .amount_options > input { width: 274px; max-width: 66%; display: block; margin: 10px auto; height: 30px; transform: scale(1.5); } .donate_button { display: inline-grid; grid-template-columns: 1fr 100px auto 1fr; grid-template-rows: 80px auto; width: 400px; height: 140px; max-width: 100%; padding: 12px 0; text-align: center; color: white !important; font-weight: bold; border-radius: 8px; box-shadow: 1px 1px 0 0 rgba(255,255,255,.3) inset, 3px 3px 8px 0 rgba(0,0,0,0.4); } .small_note { font-size: large; grid-area: 2 / 3 / 3 / 4; } .price_note { grid-area: 1/3/2/4; font-size:68px; } @media screen and (max-width: 450px) { .donate_button{ grid-template-columns: 1fr 80px auto 1fr; } .price_note{font-size:60px;} } .donate_icon { grid-area: 1/1/-1/3; width: 85%; max-width: 100px; margin: auto; max-height: 100px; } .fa-secondary{fill:#fdd835;} .payment-marks { display: flex; width: 400px; max-width: 100%; margin: auto; justify-content: right; } .payment-marks img{ height: 30px; margin-right: 6px; border: none; width: auto; } </style> <svg display="none"> <defs> <symbol viewBox="0 0 576 512" id="pg-bank"> <!-- Font Awesome Pro 5.15.4 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license (Commercial License) --> <path d="M560 224h-29.51a159.88 159.88 0 0 0-37.38-52.46L512 96h-32c-29.4 0-55.39 13.5-73 34.32-7.57-1.1-15.12-2.32-23-2.32H256c-94.82 0-160 78.88-160 160a159.75 159.75 0 0 0 64 128v80a16 16 0 0 0 16 16h64a16 16 0 0 0 16-16v-48h128v48a16 16 0 0 0 16 16h64a16 16 0 0 0 16-16v-80.72A160.37 160.37 0 0 0 511.28 384H560a16 16 0 0 0 16-16V240a16 16 0 0 0-16-16zm-128 64a16 16 0 1 1 16-16 16 16 0 0 1-16 16z" class="fa-secondary"/><path d="M51.26 255.52a24 24 0 0 1-18.74-28.3C34.74 215.82 45.4 208 57 208h1a6 6 0 0 0 6-6v-20a6 6 0 0 0-6-6C29.5 176 4.1 196.4.47 224.62a54.64 54.64 0 0 0-.47 7.23A56.08 56.08 0 0 0 56 288h40a155.05 155.05 0 0 1 3.37-32H56a23.63 23.63 0 0 1-4.74-.48zM432 256a16 16 0 1 0 16 16 16 16 0 0 0-16-16zM306.5 0a96 96 0 0 0-88.81 132.51A162.64 162.64 0 0 1 256 128h128a104.31 104.31 0 0 1 12.71.88A96.06 96.06 0 0 0 306.5 0z" class="fa-primary"/> </symbol> </defs> </svg> <div id="donate_title_close" class="donate_title" style="display:none;"> ▼サーバー運営を助ける支援をお願いします </div> <div id="donate_title_open" class="donate_title"> ▼サーバー運営を助ける支援をお願いします </div> <div id="donate_large"> <div style="text-align: center; display: grid; grid-template-columns: 30px 20px auto 20px auto; grid-template-rows: auto auto; width: 400px; max-width: 100%; margin: auto;"> <div class="amount_options" id="onetime"> <a id="donate_link" onclick="localStorage.setItem('donateClickDate', new Date().toString());dataLayer.push({'event': 'donateLinkClicked'});" href="https://donate.stripe.com/5kA5msbys6u42Gs14k" target="_blank" class="button-exp-style donate_button"> <svg class="donate_icon"><use xlink:href="#pg-bank"></svg> <span class="price_note">900円</span><span class="small_note">(うち手数料33円)</span></a> <input type="range" min="1" max="7" value="3" id="donation_slider"> <script> const priceData = [ { text: `<svg class="donate_icon"><use xlink:href="#pg-bank"></svg><span class="price_note">300円</span><span class="small_note">(うち手数料11円)</span>`, url: "https://donate.stripe.com/fZe4io1XSbOo0yk9AB" }, { text: `<svg class="donate_icon"><use xlink:href="#pg-bank"></svg><span class="price_note">600円</span><span class="small_note">(うち手数料22円)</span>`, url: "https://donate.stripe.com/00g4io6e8bOo1CoeV9" }, { text: `<svg class="donate_icon"><use xlink:href="#pg-bank"></svg><span class="price_note">900円</span><span class="small_note">(うち手数料33円)</span>`, url: "https://donate.stripe.com/5kA5msbys6u42Gs14k" }, { text: `<svg class="donate_icon"><use xlink:href="#pg-bank"></svg><span class="price_note">1500円</span><span class="small_note">(うち手数料54円)</span>`, url: "https://donate.stripe.com/14k4io7ic6u46WI147" }, { text: `<svg class="donate_icon"><use xlink:href="#pg-bank"></svg><span class="price_note">3000円</span><span class="small_note">(うち手数料108円)</span>`, url: "https://donate.stripe.com/28o6qweKE9GgftedQV" }, { text: `<svg class="donate_icon"><use xlink:href="#pg-bank"></svg><span class="price_note">5000円</span><span class="small_note">(うち手数料180円)</span>`, url: "https://donate.stripe.com/bIY4ioauo7y8ch24gm" }, { text: `<svg class="donate_icon"><use xlink:href="#pg-bank"></svg><span class="price_note">1万円</span><span class="small_note">(うち手数料360円)</span>`, url: "https://donate.stripe.com/cN25ms1XSf0AfteeV1" }, { text: `<svg class="donate_icon"><use xlink:href="#pg-bank"></svg><span class="price_note">テスト</span><span class="small_note">(うち手数料XX円)</span>`, url: "https://donate.stripe.com/test_7sIaIy7n5arT90c7st" } ]; const slider = document.getElementById("donation_slider"); const button = document.getElementById("donate_link"); slider.addEventListener("input", e=>{ button.innerHTML = priceData[e.target.value - 1].text; button.href = priceData[e.target.value - 1].url + window.location.search; }); button.href = button.href + window.location.search; </script> </div> </div> <div class="payment-marks"> <img src="https://i.gzn.jp/img/gsc/payment/visa.png" border="0"> <img src="https://i.gzn.jp/img/gsc/payment/master.png" border="0"> <img src="https://i.gzn.jp/img/gsc/payment/amex.png" border="0"> <img src="https://i.gzn.jp/img/gsc/payment/apple-pay.svg" border="0"> <img src="https://i.gzn.jp/img/gsc/payment/g-pay.png" border="0"> </div> <p> 私たちは過去最高の大ヒットを記録しているのと裏腹に価格高騰などの影響でサーバー運営が非常に苦しい状態です。打てる手は全て打ちましたが、それでもまだ危機的状況にあります。なので、GIGAZINEの物理的なサーバーたちを、たった1円でも良いので読者の皆さまに支援してもらえればとっても助かります!今すぐ1回払いの寄付は上のボタンから、毎月寄付は<b><a href="https://gigazine.net/club/" target="_blank">こちらのリンク先から!</a></b><br> <br> ・<b><a href="https://gigazine.net/sponsor/20250101-thank-you/" target="_blank">これまでGIGAZINEを支援してくれたメンバーのリスト</a></b> </p> </div> </div> <script> (async ()=>{ let adblock = false; const url = "https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js"; try { const response = await fetch(url); } catch (error) { adblock = true; } const showDonateWrapper = ()=>{ donationWrapper.style.display = "block"; } const donationWrapper = document.getElementById('donation_wrapper'); if(/https:\/\/gigazine.net\/news\/about\/*/.test(location.href)){showDonateWrapper();} else if(!window.localStorage.getItem("landing_flag_donate")){ window.localStorage.setItem("landing_flag_donate", Date.now()); } else { if( /show_donation=true/.test(location.href) 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{googletag.display('div-gpt-ad-1533535919247-0');}); </script> </div> </div> </div> <!-- 20141031_GSC_3 --> <div class="cntimage"><!-- ←20140212 --> <div style="width:100%;position:relative;"> <time datetime="2023-11-14T11:05:00+09:00" class="yeartime">2023年11月14日 11時05分</time> <span class="yeartime p-category"><a href="/news/C6/">ハードウェア</a></span> </div> <style> .p-category{ position: absolute; right: 2px; } </style> <h1 class="title">NVIDIAがAIおよびHPC向けGPU「H200」を発表、H100と比べて推論速度が2倍でHPC性能はx86 CPUの110倍</h1> <!-- google_ad_section_start --> <p class="preface"></p><a href="https://i.gzn.jp/img/2023/11/14/nvidia-h200-hopper/00.jpg" target="_blank"><img src="https://i.gzn.jp/img/2023/11/14/nvidia-h200-hopper/00_m.jpg" border="0" class="lzsmall img-standard-size" alt=""></a><p class="preface"> <br /> NVIDIAがAIおよびハイパフォーマンス・コンピューティング(HPC)向けのGPU「<b>H200</b>」を2023年11月13日(月)に発表しました。H200は前世代モデル「<b><a href="https://gigazine.net/news/20220323-nvidia-hopper-h100-gpu/" target="_blank">H100</a></b>」と比べて推論速度が2倍、x86 CPUと比べてHPC性能が110倍になっていることがアピールされています。<br /> <br /> <b>H200 Tensor Core GPU | NVIDIA</b><br /> <b><a href="https://www.nvidia.com/en-us/data-center/h200/" target="_blank">https://www.nvidia.com/en-us/data-center/h200/</a></b><br /> </p><a href="https://www.nvidia.com/en-us/data-center/h200/" target="_blank"><img src="https://i.gzn.jp/img/2023/11/14/nvidia-h200-hopper/02.png" border="0" class="lzsmall" alt=""></a><p class="preface"> <br /> <b>NVIDIA Supercharges Hopper, the World’s Leading AI Computing Platform | NVIDIA Newsroom</b><br /> <b><a href="https://nvidianews.nvidia.com/news/nvidia-supercharges-hopper-the-worlds-leading-ai-computing-platform" target="_blank">https://nvidianews.nvidia.com/news/nvidia-supercharges-hopper-the-worlds-leading-ai-computing-platform</a></b><br /> <br /> H200はGPUアーキテクチャ「Hopper」を採用したGPUで、AI処理やHPC向けに最適化されています。同じくHopperを採用した前世代モデルのH100はAIの開発に必要な推論および学習において非常に高い性能を発揮し、<b><a href="https://gigazine.net/news/20230803-gpu-supply-demand/" target="_blank">需要過多によって製品不足に陥る</a></b>ほどの人気を集めました。<br /> <br /> 新たに発表されたH200には高速かつ大容量なメモリ技術「HBM3e」が採用されており、メモリ容量は141GB、メモリ帯域幅は4.8TB毎秒に達しています。H200は高性能なメモリを採用することでH100と比べて2倍の推論速度を実現しているとのこと。NVIDIAが示したH100(薄緑)とH200(緑)の推論速度比較グラフを確認すると、H200はH100と比較してLlama2 13Bで1.4倍、GPT-3 175Bで1.6倍、Llama2 70Bで1.9倍の推論速度を達成していることが分かります。<br /> </p><a href="https://i.gzn.jp/img/2023/11/14/nvidia-h200-hopper/04.png" target="_blank"><img src="https://i.gzn.jp/img/2023/11/14/nvidia-h200-hopper/04_m.png" border="0" class="lzsmall" alt=""></a><p class="preface"> <br /> また、HPC性能はX86 CPUの110倍で、NVIDIA製GPU「<b><a href="https://gigazine.net/news/20200515-nvidia-ampere-a100/" target="_blank">A100</a></b>」と比べても2倍高性能であることが示されています。<br /> </p><a href="https://i.gzn.jp/img/2023/11/14/nvidia-h200-hopper/05.png" target="_blank"><img src="https://i.gzn.jp/img/2023/11/14/nvidia-h200-hopper/05_m.png" border="0" class="lzsmall" alt=""></a><p class="preface"> <br /> さらに、H200の運用費用や消費エネルギーはH100と比べて半減していることもアピールされています。<br /> </p><a href="https://i.gzn.jp/img/2023/11/14/nvidia-h200-hopper/06.png" target="_blank"><img src="https://i.gzn.jp/img/2023/11/14/nvidia-h200-hopper/06_m.png" border="0" class="lzsmall" alt=""></a><p class="preface"> <br /> H200の詳細な仕様は以下の通り。<br /> </p><div class="sheet-wrapper"><table class="sheet"><tbody><tr><th>フォームファクタ</th><th>H200 SXM</th></tr><tr><td>FP64</td><td>34&nbsp;TFLOPS</td></tr><tr><td>FP64 Tensor Core</td><td>67&nbsp;TFLOPS</td></tr><tr><td>FP32</td><td>67&nbsp;TFLOPS</td></tr><tr><td>TF32 Tensor Core</td><td>989&nbsp;TFLOPS</td></tr><tr><td>BFLOAT16 Tensor Core</td><td>1979&nbsp;TFLOPS</td></tr><tr><td>FP16 Tensor Core</td><td>1979&nbsp;TFLOPS</td></tr><tr><td>FP8 Tensor Core</td><td>3958&nbsp;TFLOPS</td></tr><tr><td>INT8 Tensor Core</td><td>3958&nbsp;TFLOPS</td></tr><tr><td>メモリ容量</td><td>141GB</td></tr><tr><td>メモリ帯域幅</td><td>4.8TB/s</td></tr><tr><td>デコーダー</td><td>7 NVDEC<br>7 JPEG</td></tr><tr><td>消費電力</td><td>最大700W</td></tr><tr><td>マルチインスタンス GPU(MIG)</td><td>Up to 7 MIGs @16.5GB each</td></tr><tr><td>フォームファクタ</td><td>SXM</td></tr><tr><td>接続性</td><td>NVIDIA NVLink: 900GB/s<br>PCIe Gen5: 128GB/s</td></tr></tbody></table></div><p class="preface"> <br /> なお、H200は4ウェイもしくは8ウェイ構成のNVIDIA HGX H200サーバーボードとして提供される予定で、AIおよびHPC特化型チップシステム「<b><a href="https://www.nvidia.com/ja-jp/data-center/grace-hopper-superchip/" target="_blank">NVIDIA GH200 Grace Hopper Superchip</a></b>」に組み込むことも可能とされています。<br /> </p> <!-- google_ad_section_end --> <div style="text-align: center; margin: 10px auto 0px;"><div id="URLClip" style="width:300px; height:40px; border: 2px solid #bbb; border-radius: 3px; background: white; padding: 8px 0; text-align: center; position: relative; cursor: pointer; color: #222; font-size: 14px; font-weight: 700; display: inline-block; vertical-align: middle;">この記事のタイトルとURLをコピーする</div></div> </div><!-- ←20140212 --> <!-- gpublic_ASO --> <div id="EndFooter"> <div 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= "https://pagead2.googlesyndication.com/pagead/js/adsbygoogle.js"; try { const response = await fetch(url); } catch (error) { adblock = true; } const sentDate = window.localStorage.getItem("donate_footer_sentDate"); const closeDate = window.localStorage.getItem("donate_footer_closeDate"); const donateFooter = document.getElementById("donate_footer"); const donateFooterScroll = ()=>{ if(window.scrollY >= 300){ donateFooter.classList.remove("downMove"); donateFooter.classList.add("upMove"); } else { if(donateFooter.classList.contains("upMove")){ donateFooter.classList.remove("upMove"); donateFooter.classList.add("downMove"); } } }; if(!window.localStorage.getItem("landing_flag_donate_footer")){ window.localStorage.setItem("landing_flag_donate_footer", Date.now()); } else { if( /show_donation=true/.test(location.href) || (adblock && new Date("2025/01/06 10:25") < Date.now() && new Date("2025/01/27 09:00") > Date.now())){ if(!sentDate && closeDate < (Date.now() - 1000 * 3600 * 24)){ donateFooter.style.display = "grid"; document.addEventListener("scroll", donateFooterScroll); } } } const donateFooterClose = document.getElementById("donate_footer_close"); donateFooterClose.addEventListener("click", ()=>{ document.removeEventListener("scroll", donateFooterScroll); donateFooter.classList.remove("upMove"); donateFooter.classList.add("downMove"); window.localStorage.setItem("donate_footer_closeDate", Date.now()); window.setTimeout(()=>{ donateFooter.style.display = "none"; }, 1000); }); const donateDialog = document.getElementById("donate_dialog"); const donateFooterDelay = document.getElementById("donate_footer_delay"); donateFooterDelay.addEventListener("click", ()=>{donateDialog.showModal();}); const donateDialogClose = document.getElementById("donate_dialog_close"); donateDialogClose.addEventListener("click", ()=>{donateDialog.close();}); const donateFooterKifu = document.getElementById("donate_footer_kifu"); donateFooterKifu.addEventListener("click", ()=>{ document.removeEventListener("scroll", donateFooterScroll); window.localStorage.setItem("donate_footer_sentDate", Date.now()); donateFooter.classList.remove("upMove"); donateFooter.classList.add("downMove"); window.setTimeout(()=>{ donateFooter.style.display = "none"; }, 1000); }); const donateRemindForm = document.getElementById("donate_remind_form"); donateRemindForm.addEventListener("submit", ()=>{ donateDialog.close(); document.removeEventListener("scroll", donateFooterScroll); donateFooter.innerHTML = "<p>送信しました</p>"; window.localStorage.setItem("donate_footer_sentDate", Date.now()); window.setTimeout(()=>{ donateFooter.classList.remove("upMove"); donateFooter.classList.add("downMove"); window.setTimeout(()=>{ donateFooter.style.display = "none"; }, 1000); }, 1000); }); })() </script> </body> </html><!-- comments_no_login -->

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