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update docs for 0.20.0rc2 Signed-off-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
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<li class="toctree-l1"><a class="reference internal" href="../key-features.html">Key Features</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../torch.html">PyTorch Backend</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../installation/grace-hopper.html">Installing on Grace Hopper</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../llm-api/index.html">API Introduction</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_inference_kv_events.html">Get KV Cache Events</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_inference_customize.html">Generate text with customization</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_lookahead_decoding.html">Generate Text Using Lookahead Decoding</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_medusa_decoding.html">Generate Text Using Medusa Decoding</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_guided_decoding.html">Generate text with guided decoding</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_logits_processor.html">Control generated text using logits processor</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_quantization.html">Generation with Quantization</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_inference.html">Generate text</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_multilora.html">Generate text with multiple LoRA adapters</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_inference_async_streaming.html">Generate Text in Streaming</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_inference_distributed.html">Distributed LLM Generation</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_eagle_decoding.html">Generate Text Using Eagle Decoding</a></li>
|
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_auto_parallel.html">Automatic Parallelism with LLM</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_mgmn_llm_distributed.html">Llm Mgmn Llm Distributed</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_mgmn_trtllm_bench.html">Llm Mgmn Trtllm Bench</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_mgmn_trtllm_serve.html">Llm Mgmn Trtllm Serve</a></li>
|
||
</ul>
|
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</details></li>
|
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<li class="toctree-l1"><a class="reference internal" href="../examples/customization.html">LLM Common Customizations</a></li>
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<li class="toctree-l1 has-children"><a class="reference internal" href="../examples/llm_api_examples.html">LLM Examples</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_inference_async.html">Generate Text Asynchronously</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_inference_kv_events.html">Get KV Cache Events</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_inference_customize.html">Generate text with customization</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_lookahead_decoding.html">Generate Text Using Lookahead Decoding</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_medusa_decoding.html">Generate Text Using Medusa Decoding</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_guided_decoding.html">Generate text with guided decoding</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_logits_processor.html">Control generated text using logits processor</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_quantization.html">Generation with Quantization</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_inference.html">Generate text</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_multilora.html">Generate text with multiple LoRA adapters</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_inference_async_streaming.html">Generate Text in Streaming</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_inference_distributed.html">Distributed LLM Generation</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_eagle_decoding.html">Generate Text Using Eagle Decoding</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_auto_parallel.html">Automatic Parallelism with LLM</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_mgmn_llm_distributed.html">Llm Mgmn Llm Distributed</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_mgmn_trtllm_bench.html">Llm Mgmn Trtllm Bench</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_mgmn_trtllm_serve.html">Llm Mgmn Trtllm Serve</a></li>
|
||
</ul>
|
||
</details></li>
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<li class="toctree-l1"><a class="reference internal" href="H100vsA100.html">H100 has 4.6x A100 Performance in TensorRT-LLM, achieving 10,000 tok/s at 100ms to first token</a></li>
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<li class="toctree-l1 current active"><a class="current reference internal" href="#">H200 achieves nearly 12,000 tokens/sec on Llama2-13B with TensorRT-LLM</a></li>
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<li class="toctree-l1"><a class="reference internal" href="Falcon180B-H200.html">Falcon-180B on a single H200 GPU with INT4 AWQ, and 6.7x faster Llama-70B over A100</a></li>
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<li class="toctree-l1"><a class="reference internal" href="XQA-kernel.html">New XQA-kernel provides 2.4x more Llama-70B throughput within the same latency budget</a></li>
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<li class="breadcrumb-item active" aria-current="page"><span class="ellipsis">H200 achieves nearly 12,000 tokens/sec on Llama2-13B with TensorRT-LLM</span></li>
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<p>:loudspeaker: Note: The below data is using TensorRT-LLM v0.5. There have been significant improvements in v0.6 & later. Please see updated Llama performance <a class="reference internal" href="Falcon180B-H200.html"><span class="std std-doc">here</span></a>.</p>
|
||
<section id="h200-achieves-nearly-12-000-tokens-sec-on-llama2-13b-with-tensorrt-llm">
|
||
<h1>H200 achieves nearly 12,000 tokens/sec on Llama2-13B with TensorRT-LLM<a class="headerlink" href="#h200-achieves-nearly-12-000-tokens-sec-on-llama2-13b-with-tensorrt-llm" title="Link to this heading">#</a></h1>
|
||
<p>TensorRT-LLM evaluation of the <a class="reference external" href="https://nvidianews.nvidia.com/news/nvidia-supercharges-hopper-the-worlds-leading-ai-computing-platform">new H200 GPU</a> achieves <strong>11,819 tokens/s on Llama2-13B</strong> on a single GPU. H200 is up to <strong>1.9x faster</strong> than H100. This performance is enabled by H200’s larger, faster <a class="reference internal" href="#latest-hbm-memory">HBM3e memory</a>.</p>
|
||
<p><strong>H200 FP8 Max throughput</strong></p>
|
||
<div class="pst-scrollable-table-container"><table class="table">
|
||
<thead>
|
||
<tr class="row-odd"><th class="head text-left"><p>Model</p></th>
|
||
<th class="head text-left"><p>Batch Size<sup>(1)</sup></p></th>
|
||
<th class="head text-left"><p>TP<sup>(2)</sup></p></th>
|
||
<th class="head text-left"><p>Input Length</p></th>
|
||
<th class="head text-left"><p>Output Length</p></th>
|
||
<th class="head text-right"><p>Throughput (out tok/s/GPU)</p></th>
|
||
</tr>
|
||
</thead>
|
||
<tbody>
|
||
<tr class="row-even"><td class="text-left"><p>llama_13b</p></td>
|
||
<td class="text-left"><p>1024</p></td>
|
||
<td class="text-left"><p>1</p></td>
|
||
<td class="text-left"><p>128</p></td>
|
||
<td class="text-left"><p>128</p></td>
|
||
<td class="text-right"><p>11,819</p></td>
|
||
</tr>
|
||
<tr class="row-odd"><td class="text-left"><p>llama_13b</p></td>
|
||
<td class="text-left"><p>128</p></td>
|
||
<td class="text-left"><p>1</p></td>
|
||
<td class="text-left"><p>128</p></td>
|
||
<td class="text-left"><p>2048</p></td>
|
||
<td class="text-right"><p>4,750</p></td>
|
||
</tr>
|
||
<tr class="row-even"><td class="text-left"><p>llama_13b</p></td>
|
||
<td class="text-left"><p>64</p></td>
|
||
<td class="text-left"><p>1</p></td>
|
||
<td class="text-left"><p>2048</p></td>
|
||
<td class="text-left"><p>128</p></td>
|
||
<td class="text-right"><p>1,349</p></td>
|
||
</tr>
|
||
<tr class="row-odd"><td class="text-left"><p>llama_70b</p></td>
|
||
<td class="text-left"><p>512</p></td>
|
||
<td class="text-left"><p>1</p></td>
|
||
<td class="text-left"><p>128</p></td>
|
||
<td class="text-left"><p>128</p></td>
|
||
<td class="text-right"><p>3,014</p></td>
|
||
</tr>
|
||
<tr class="row-even"><td class="text-left"><p>llama_70b</p></td>
|
||
<td class="text-left"><p>512</p></td>
|
||
<td class="text-left"><p>2</p></td>
|
||
<td class="text-left"><p>128</p></td>
|
||
<td class="text-left"><p>2048</p></td>
|
||
<td class="text-right"><p>1,654</p></td>
|
||
</tr>
|
||
<tr class="row-odd"><td class="text-left"><p>llama_70b</p></td>
|
||
<td class="text-left"><p>64</p></td>
|
||
<td class="text-left"><p>1</p></td>
|
||
<td class="text-left"><p>2048</p></td>
|
||
<td class="text-left"><p>128</p></td>
|
||
<td class="text-right"><p>341</p></td>
|
||
</tr>
|
||
<tr class="row-even"><td class="text-left"><p>llama_70b</p></td>
|
||
<td class="text-left"><p>32</p></td>
|
||
<td class="text-left"><p>1</p></td>
|
||
<td class="text-left"><p>2048</p></td>
|
||
<td class="text-left"><p>128</p></td>
|
||
<td class="text-right"><p>303</p></td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
</div>
|
||
<p><sub>Preliminary measured performance, subject to change. TensorRT-LLM v0.5.0, TensorRT v9.1.0.4 | H200, H100 FP8. </sub></p>
|
||
<p><sup><em>(1) Largest batch supported on given TP configuration by power of 2.</em></sup> <sup><em>(2) TP = Tensor Parallelism</em></sup></p>
|
||
<p>Additional Performance data is available on the <a class="reference external" href="https://developer.nvidia.com/deep-learning-performance-training-inference/ai-inference">NVIDIA Data Center Deep Learning Product Performance</a> page, & soon in <a class="reference external" href="https://nvidia.github.io/TensorRT-LLM/performance.html">TensorRT-LLM’s Performance Documentation</a>.</p>
|
||
<section id="h200-vs-h100">
|
||
<h2>H200 vs H100<a class="headerlink" href="#h200-vs-h100" title="Link to this heading">#</a></h2>
|
||
<p>H200’s HBM3e larger capacity & faster memory enables up to <strong>1.9x</strong> performance on LLMs compared to H100. Max throughput improves due to its dependence on memory capacity and bandwidth, benefitting from the new HBM3e. First token latency is compute bound for most ISLs, meaning H200 retains similar time to first token as H100.</p>
|
||
<p>For practical examples of H200’s performance:</p>
|
||
<p><strong>Max Throughput TP1:</strong>
|
||
an offline summarization scenario (ISL/OSL=2048/128) with Llama-70B on a single H200 is 1.9x more performant than H100.</p>
|
||
<p><strong>Max Throughput TP8:</strong>
|
||
an online chat agent scenario (ISL/OSL=80/200) with GPT3-175B on a full HGX (TP8) H200 is 1.6x more performant than H100.</p>
|
||
<img src="https://github.com/NVIDIA/TensorRT-LLM/blob/rel/docs/source/blogs/media/H200launch_tps.png?raw=true" alt="H200 TPS" width="500" height="auto">
|
||
<p><sub>Preliminary measured performance, subject to change.
|
||
TensorRT-LLM v0.5.0, TensorRT v9.1.0.4. | Llama-70B: H100 FP8 BS 8, H200 FP8 BS 32 | GPT3-175B: H100 FP8 BS 64, H200 FP8 BS 128 </sub></p>
|
||
<p><strong>Max Throughput across TP/BS:</strong>
|
||
Max throughput<sup>(3)</sup> on H200 vs H100 varies by model, sequence lengths, BS, and TP. Below results shown for maximum throughput per GPU across all these variables.</p>
|
||
<img src="https://github.com/NVIDIA/TensorRT-LLM/blob/rel/docs/source/blogs/media/H200launch_H200vsH100_tps.png?raw=true" alt="max throughput llama sweep" width="500" height="auto">
|
||
<p><sub>Preliminary measured performance, subject to change.
|
||
TensorRT-LLM v0.5.0, TensorRT v9.1.0.4 | H200, H100 FP8. </sub></p>
|
||
<p><sup><em>(3) Max Throughput per GPU is defined as the highest tok/s per GPU, swept across TP configurations & BS powers of 2.</em></sup></p>
|
||
</section>
|
||
<section id="latest-hbm-memory">
|
||
<h2>Latest HBM Memory<a class="headerlink" href="#latest-hbm-memory" title="Link to this heading">#</a></h2>
|
||
<p>H200 is the newest addition to NVIDIA’s data center GPU portfolio. To maximize that compute performance, H200 is the first GPU with HBM3e memory with 4.8TB/s of memory bandwidth, a 1.4X increase over H100. H200 also expands GPU memory capacity nearly 2X to 141 gigabytes (GB). The combination of faster and larger HBM memory accelerates performance of LLM model inference performance with faster throughput and tokens per second. These results are measured and preliminary, more updates expected as optimizations for H200 continue with TensorRT-LLM.</p>
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</section>
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