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<div class="bd-toc-item navbar-nav"><p aria-level="2" class="caption" role="heading"><span class="caption-text">Getting Started</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../overview.html">Overview</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../quick-start-guide.html">Quick Start Guide</a></li>
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<li class="toctree-l1 has-children"><a class="reference internal" href="../installation/index.html">Installation</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="../installation/containers.html">Pre-built release container images on NGC</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../installation/linux.html">Installing on Linux via <code class="docutils literal notranslate"><span class="pre">pip</span></code></a></li>
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<li class="toctree-l2"><a class="reference internal" href="../installation/build-from-source-linux.html">Building from Source Code on Linux</a></li>
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</ul>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Deployment Guide</span></p>
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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.html">Generate text</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_inference_async.html">Generate text asynchronously</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_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_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_sparse_attention.html">Sparse Attention</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_speculative_decoding.html">Speculative Decoding</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_kv_cache_connector.html">KV Cache Connector</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_kv_cache_offloading.html">KV Cache Offloading</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_runtime.html">Runtime Configuration Examples</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_sampling.html">Sampling Techniques Showcase</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_mgmn_llm_distributed.html">Run LLM-API with pytorch backend on Slurm</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_mgmn_trtllm_bench.html">Run trtllm-bench with pytorch backend on Slurm</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/llm_mgmn_trtllm_serve.html">Run trtllm-serve with pytorch backend on Slurm</a></li>
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</ul>
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</details></li>
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<li class="toctree-l1 has-children"><a class="reference internal" href="../examples/trtllm_serve_examples.html">Online Serving 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/curl_chat_client.html">Curl Chat Client</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/curl_chat_client_for_multimodal.html">Curl Chat Client For Multimodal</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/curl_completion_client.html">Curl Completion Client</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/deepseek_r1_reasoning_parser.html">Deepseek R1 Reasoning Parser</a></li>
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||
<li class="toctree-l2"><a class="reference internal" href="../examples/genai_perf_client.html">Genai Perf Client</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/genai_perf_client_for_multimodal.html">Genai Perf Client For Multimodal</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/openai_chat_client.html">OpenAI Chat Client</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/openai_chat_client_for_multimodal.html">OpenAI Chat Client for Multimodal</a></li>
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<li class="toctree-l2"><a class="reference internal" href="../examples/openai_completion_client.html">OpenAI Completion Client</a></li>
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||
<li class="toctree-l2"><a class="reference internal" href="../examples/openai_completion_client_for_lora.html">Openai Completion Client For Lora</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../examples/openai_completion_client_json_schema.html">OpenAI Completion Client with JSON Schema</a></li>
|
||
</ul>
|
||
</details></li>
|
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<li class="toctree-l1"><a class="reference internal" href="../examples/dynamo_k8s_example.html">Dynamo K8s Example</a></li>
|
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<li class="toctree-l1 has-children"><a class="reference internal" href="../deployment-guide/index.html">Model Recipes</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="../deployment-guide/deployment-guide-for-deepseek-r1-on-trtllm.html">Deployment Guide for DeepSeek R1 on TensorRT LLM - Blackwell & Hopper Hardware</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../deployment-guide/deployment-guide-for-llama3.3-70b-on-trtllm.html">Deployment Guide for Llama3.3 70B on TensorRT LLM - Blackwell & Hopper Hardware</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../deployment-guide/deployment-guide-for-llama4-scout-on-trtllm.html">Deployment Guide for Llama4 Scout 17B on TensorRT LLM - Blackwell & Hopper Hardware</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../deployment-guide/deployment-guide-for-gpt-oss-on-trtllm.html">Deployment Guide for GPT-OSS on TensorRT-LLM - Blackwell Hardware</a></li>
|
||
<li class="toctree-l2"><a class="reference internal" href="../deployment-guide/deployment-guide-for-qwen3-next-on-trtllm.html">Deployment Guide for Qwen3 Next on TensorRT LLM - Blackwell & Hopper Hardware</a></li>
|
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|
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|
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<li class="toctree-l1"><a class="reference internal" href="tech_blog/blog12_Combining_Guided_Decoding_and_Speculative_Decoding.html">Combining Guided Decoding and Speculative Decoding: Making CPU and GPU Cooperate Seamlessly</a></li>
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<li class="toctree-l1"><a class="reference internal" href="tech_blog/blog14_Scaling_Expert_Parallelism_in_TensorRT-LLM_part3.html">Scaling Expert Parallelism in TensorRT LLM (Part 3: Pushing the Performance Boundary)</a></li>
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<li class="toctree-l1"><a class="reference internal" href="tech_blog/blog1_Pushing_Latency_Boundaries_Optimizing_DeepSeek-R1_Performance_on_NVIDIA_B200_GPUs.html">Pushing Latency Boundaries: Optimizing DeepSeek-R1 Performance on NVIDIA B200 GPUs</a></li>
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<li class="toctree-l1"><a class="reference internal" href="tech_blog/blog3_Optimizing_DeepSeek_R1_Throughput_on_NVIDIA_Blackwell_GPUs.html">Optimizing DeepSeek R1 Throughput on NVIDIA Blackwell GPUs: A Deep Dive for Developers</a></li>
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<li class="toctree-l1"><a class="reference internal" href="tech_blog/blog4_Scaling_Expert_Parallelism_in_TensorRT-LLM.html">Scaling Expert Parallelism in TensorRT LLM (Part 1: Design and Implementation of Large-scale EP)</a></li>
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<li class="toctree-l1"><a class="reference internal" href="tech_blog/blog6_Llama4_maverick_eagle_guide.html">How to launch Llama4 Maverick + Eagle3 TensorRT LLM server</a></li>
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<li class="toctree-l1"><a class="reference internal" href="tech_blog/blog8_Scaling_Expert_Parallelism_in_TensorRT-LLM_part2.html">Scaling Expert Parallelism in TensorRT LLM (Part 2: Performance Status and Optimization)</a></li>
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<li class="toctree-l1"><a class="reference internal" href="tech_blog/blog9_Deploying_GPT_OSS_on_TRTLLM.html">Running a High Performance GPT-OSS-120B Inference Server with TensorRT LLM</a></li>
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<li class="toctree-l1"><a class="reference internal" href="Best_perf_practice_on_DeepSeek-R1_in_TensorRT-LLM.html">How to get best performance on DeepSeek-R1 in TensorRT LLM</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="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="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="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>
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<section class="tex2jax_ignore mathjax_ignore" 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/latest/performance/perf-overview.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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</section>
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