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<li class="toctree-l1"><a class="reference internal" href="../advanced/gpt-attention.html">Multi-Head, Multi-Query, and Group-Query Attention</a></li>
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<li class="toctree-l2"><a class="reference internal" href="#table-of-contents">Table of Contents</a></li>
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<p class="caption" role="heading"><span class="caption-text">Reference</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../reference/troubleshooting.html">Troubleshooting</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../reference/precision.html">Numerical Precision</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../reference/memory.html">Memory Usage of TensorRT-LLM</a></li>
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<p class="caption" role="heading"><span class="caption-text">Blogs</span></p>
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<ul>
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<li class="toctree-l1"><a class="reference internal" href="../blogs/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"><a class="reference internal" href="../blogs/H200launch.html">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="../blogs/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="../blogs/quantization-in-TRT-LLM.html">Speed up inference with SOTA quantization techniques in TRT-LLM</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../blogs/XQA-kernel.html">New XQA-kernel provides 2.4x more Llama-70B throughput within the same latency budget</a></li>
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<section id="introduction">
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<span id="perf-best-practice"></span><h1>Introduction<a class="headerlink" href="#introduction" title="Link to this heading"></a></h1>
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<p>While defaults are expected to provide solid performance, TensorRT-LLM has several configurable options that can improve performance for your particular workload. This guide is meant to help you tune TensorRT-LLM to extract the best performance for your use case. It covers several of the most helpful tunable parameters and provides intuition for thinking about them. This guide also doubles as an example of how to work with TensorRT-LLM’s LLM-API and its TRTLLM-Bench benchmarking workflow.</p>
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<p>This guide uses Llama-3.3-70b on 4 H100-sxm-80GB connected via NVLink as a case study and focuses on optimizing performance on input sequence length/output sequence length of 2048/2048. Case study sections throughout this guide reference internal performance testing and results to help reinforce the conclusions and recommendations given.</p>
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<section id="prerequisite-knowledge">
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<h2>Prerequisite Knowledge<a class="headerlink" href="#prerequisite-knowledge" title="Link to this heading"></a></h2>
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<p>This guide expects you have some familiarity with the following concepts</p>
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<ul class="simple">
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<li><p>Phases of Inference: Context (Prefill) Phase and Generation Phase</p></li>
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<li><p>Inflight Batching</p></li>
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<li><p>Tensor Parallelism and Pipeline Parallelism</p></li>
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<li><p>Quantization</p></li>
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</ul>
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<p>Please refer to <a class="reference external" href="https://developer.nvidia.com/blog/mastering-llm-techniques-inference-optimization/">Mastering LLM Techniques - Inference Optimization</a> for an introduction to these concepts.</p>
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</section>
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<section id="table-of-contents">
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<h2>Table of Contents<a class="headerlink" href="#table-of-contents" title="Link to this heading"></a></h2>
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<ol class="arabic simple">
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<li><p><a class="reference internal" href="benchmarking-default-performance.html"><span class="std std-doc">Benchmarking Default Performance</span></a></p></li>
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<li><p><a class="reference internal" href="useful-build-time-flags.html"><span class="std std-doc">Useful Build Time Flags</span></a></p></li>
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<li><p><a class="reference internal" href="tuning-max-batch-size-and-max-num-tokens.html"><span class="std std-doc">Tuning Max-Num Tokens and Max-Batch Size</span></a></p></li>
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<li><p><a class="reference internal" href="deciding-model-sharding-strategy.html"><span class="std std-doc">Deciding Model Sharding Strategy (Tensor Parallel, Pipeline Parallel)</span></a></p></li>
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<li><p><a class="reference internal" href="fp8-quantization.html"><span class="std std-doc">FP8 Quantization</span></a></p></li>
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<li><p><a class="reference internal" href="useful-runtime-flags.html"><span class="std std-doc">Useful Runtime Options</span></a></p></li>
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