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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>
<li class="toctree-l1"><a class="reference internal" href="../quick-start-guide.html">Quick Start Guide</a></li>
<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>
<li class="toctree-l2"><a class="reference internal" href="../installation/containers.html">Pre-built release container images on NGC</a></li>
<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>
<li class="toctree-l2"><a class="reference internal" href="../installation/build-from-source-linux.html">Building from Source Code on Linux</a></li>
</ul>
</details></li>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Deployment Guide</span></p>
<ul class="nav bd-sidenav">
<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>
<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_inference_async.html">Generate text asynchronously</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_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_multilora.html">Generate text with multiple LoRA adapters</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_speculative_decoding.html">Speculative Decoding</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_kv_cache_connector.html">KV Cache Connector</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_runtime.html">Runtime Configuration Examples</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/llm_sampling.html">Sampling Techniques Showcase</a></li>
<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>
<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>
<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>
</ul>
</details></li>
<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>
<li class="toctree-l2"><a class="reference internal" href="../examples/curl_chat_client.html">Curl Chat Client</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/curl_chat_client_for_multimodal.html">Curl Chat Client For Multimodal</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/curl_completion_client.html">Curl Completion Client</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/deepseek_r1_reasoning_parser.html">Deepseek R1 Reasoning Parser</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/genai_perf_client.html">Genai Perf Client</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/genai_perf_client_for_multimodal.html">Genai Perf Client For Multimodal</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/openai_chat_client.html">OpenAI Chat Client</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/openai_chat_client_for_multimodal.html">OpenAI Chat Client for Multimodal</a></li>
<li class="toctree-l2"><a class="reference internal" href="../examples/openai_completion_client.html">OpenAI Completion Client</a></li>
<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>
<li class="toctree-l1"><a class="reference internal" href="../examples/dynamo_k8s_example.html">Dynamo K8s Example</a></li>
<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>
<li class="toctree-l2"><a class="reference internal" href="../deployment-guide/quick-start-recipe-for-deepseek-r1-on-trtllm.html">Quick Start Recipe for DeepSeek R1 on TensorRT LLM - Blackwell &amp; Hopper Hardware</a></li>
<li class="toctree-l2"><a class="reference internal" href="../deployment-guide/quick-start-recipe-for-llama3.3-70b-on-trtllm.html">Quick Start Recipe for Llama3.3 70B on TensorRT LLM - Blackwell &amp; Hopper Hardware</a></li>
<li class="toctree-l2"><a class="reference internal" href="../deployment-guide/quick-start-recipe-for-llama4-scout-on-trtllm.html">Quick Start Recipe for Llama4 Scout 17B on TensorRT LLM - Blackwell &amp; Hopper Hardware</a></li>
<li class="toctree-l2"><a class="reference internal" href="../deployment-guide/quick-start-recipe-for-gpt-oss-on-trtllm.html">Quick Start Recipe for GPT-OSS on TensorRT-LLM - Blackwell Hardware</a></li>
</ul>
</details></li>
</ul>
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Models</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../models/supported-models.html">Supported Models</a></li>
<li class="toctree-l1"><a class="reference internal" href="../models/adding-new-model.html">Adding a New Model</a></li>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">CLI Reference</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../commands/trtllm-bench.html">trtllm-bench</a></li>
<li class="toctree-l1"><a class="reference internal" href="../commands/trtllm-eval.html">trtllm-eval</a></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="../commands/trtllm-serve/index.html">trtllm-serve</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="../commands/trtllm-serve/run-benchmark-with-trtllm-serve.html">Run benchmarking with <code class="docutils literal notranslate"><span class="pre">trtllm-serve</span></code></a></li>
</ul>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">API Reference</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../llm-api/index.html">LLM API Introduction</a></li>
<li class="toctree-l1"><a class="reference internal" href="../llm-api/reference.html">API Reference</a></li>
</ul>
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Features</span></p>
<ul class="nav bd-sidenav">
<li class="toctree-l1"><a class="reference internal" href="../features/feature-combination-matrix.html">Feature Combination Matrix</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/attention.html">Multi-Head, Multi-Query, and Group-Query Attention</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/disagg-serving.html">Disaggregated Serving (Beta)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/kvcache.html">KV Cache System</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/long-sequence.html">Long Sequences</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/lora.html">LoRA (Low-Rank Adaptation)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/multi-modality.html">Multimodal Support in TensorRT LLM</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/overlap-scheduler.html">Overlap Scheduler</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/paged-attention-ifb-scheduler.html">Paged Attention, IFB, and Request Scheduling</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/parallel-strategy.html">Parallelism in TensorRT LLM</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/quantization.html">Quantization</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/sampling.html">Sampling</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/speculative-decoding.html">Speculative Decoding</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/checkpoint-loading.html">Checkpoint Loading</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/auto_deploy/auto-deploy.html">AutoDeploy (Prototype)</a></li>
</ul>
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
<ul class="nav bd-sidenav">
<li class="toctree-l1"><a class="reference internal" href="../architecture/overview.html">Architecture Overview</a></li>
<li class="toctree-l1"><a class="reference internal" href="../developer-guide/perf-analysis.html">Performance Analysis</a></li>
<li class="toctree-l1"><a class="reference internal" href="../developer-guide/perf-benchmarking.html">TensorRT LLM Benchmarking</a></li>
<li class="toctree-l1"><a class="reference internal" href="../developer-guide/ci-overview.html">Continuous Integration Overview</a></li>
<li class="toctree-l1"><a class="reference internal" href="../developer-guide/dev-containers.html">Using Dev Containers</a></li>
</ul>
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Blogs</span></p>
<ul class="nav bd-sidenav">
<li class="toctree-l1"><a class="reference internal" href="../blogs/tech_blog/blog10_ADP_Balance_Strategy.html">ADP Balance Strategy</a></li>
<li class="toctree-l1"><a class="reference internal" href="../blogs/tech_blog/blog11_GPT_OSS_Eagle3.html">Running GPT-OSS-120B with Eagle3 Speculative Decoding on GB200/B200 (TensorRT LLM)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../blogs/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>
<li class="toctree-l1"><a class="reference internal" href="../blogs/tech_blog/blog2_DeepSeek_R1_MTP_Implementation_and_Optimization.html">DeepSeek R1 MTP Implementation and Optimization</a></li>
<li class="toctree-l1"><a class="reference internal" href="../blogs/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>
<li class="toctree-l1"><a class="reference internal" href="../blogs/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>
<li class="toctree-l1"><a class="reference internal" href="../blogs/tech_blog/blog5_Disaggregated_Serving_in_TensorRT-LLM.html">Disaggregated Serving in TensorRT LLM</a></li>
<li class="toctree-l1"><a class="reference internal" href="../blogs/tech_blog/blog6_Llama4_maverick_eagle_guide.html">How to launch Llama4 Maverick + Eagle3 TensorRT LLM server</a></li>
<li class="toctree-l1"><a class="reference internal" href="../blogs/tech_blog/blog7_NGram_performance_Analysis_And_Auto_Enablement.html">N-Gram Speculative Decoding in TensorRT LLM</a></li>
<li class="toctree-l1"><a class="reference internal" href="../blogs/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>
<li class="toctree-l1"><a class="reference internal" href="../blogs/tech_blog/blog9_Deploying_GPT_OSS_on_TRTLLM.html">Running a High Performance GPT-OSS-120B Inference Server with TensorRT LLM</a></li>
<li class="toctree-l1"><a class="reference internal" href="../blogs/Best_perf_practice_on_DeepSeek-R1_in_TensorRT-LLM.html">How to get best performance on DeepSeek-R1 in TensorRT LLM</a></li>
<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>
<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>
<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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<section id="performance-analysis">
<span id="perf-analysis"></span><h1>Performance Analysis<a class="headerlink" href="#performance-analysis" title="Link to this heading">#</a></h1>
<p>NVIDIA Nsight Systems reports at the application level are highly informative. Metric sampling capabilities have increased over generations and provide a clean middle-ground between timing analysis and kernel-level deep dives with NVIDIA Nsight Compute.</p>
<p>Given the potential long runtimes of Large Languages Models (LLMs) and the diversity of workloads a model may experience during a single inference pass or binary execution, we have added features to TensorRT-LLM to get the most out of Nsight Systems capabilities. This document outlines those features as well as provides examples of how to best utilize them to understand your application.</p>
<section id="feature-descriptions">
<h2>Feature Descriptions<a class="headerlink" href="#feature-descriptions" title="Link to this heading">#</a></h2>
<p>The main functionality here:</p>
<ul class="simple">
<li><p>Relies on toggling the CUDA profiler runtime API on and off.</p></li>
<li><p>(PyTorch workflow only) Toggling the PyTorch profiler on and off.</p></li>
<li><p>Provides a means to understand which regions a user may want to focus on.</p></li>
</ul>
<p>Toggling the CUDA profiler runtime API on and off:</p>
<ul class="simple">
<li><p>Allows users to know specifically what the profiled region corresponds to.</p></li>
<li><p>Results in smaller files to post-process (for metric extraction or similar).</p></li>
</ul>
<p>(PyTorch workflow only) Toggling the PyTorch profiler on and off:</p>
<ul class="simple">
<li><p>Help users to analysis the performance breakdown in the model.</p></li>
<li><p>Results in smaller files to post-process (for metric extraction or similar).</p></li>
</ul>
</section>
<section id="coordinating-with-nvidia-nsight-systems-launch">
<h2>Coordinating with NVIDIA Nsight Systems Launch<a class="headerlink" href="#coordinating-with-nvidia-nsight-systems-launch" title="Link to this heading">#</a></h2>
<p>Consult the Nsight Systems User Guide for full overview of options.</p>
<p>On the PyTorch workflow, basic NVTX markers are by default provided. On the C++/TensorRT workflow, append <code class="docutils literal notranslate"><span class="pre">--nvtx</span></code> when calling <code class="docutils literal notranslate"><span class="pre">scripts/build_wheel.py</span></code> script to compile, and clean build the code.</p>
<section id="only-collect-specific-iterations">
<h3>Only collect specific iterations<a class="headerlink" href="#only-collect-specific-iterations" title="Link to this heading">#</a></h3>
<p>To reduce the Nsight Systems profile size, and to control that only specific iterations are collected, set environment variable <code class="docutils literal notranslate"><span class="pre">TLLM_PROFILE_START_STOP=A-B</span></code>, and append <code class="docutils literal notranslate"><span class="pre">-c</span> <span class="pre">cudaProfilerApi</span></code> to <code class="docutils literal notranslate"><span class="pre">nsys</span> <span class="pre">profile</span></code> command.</p>
</section>
<section id="enable-more-nvtx-markers-for-debugging">
<h3>Enable more NVTX markers for debugging<a class="headerlink" href="#enable-more-nvtx-markers-for-debugging" title="Link to this heading">#</a></h3>
<p>Set environment variable <code class="docutils literal notranslate"><span class="pre">TLLM_NVTX_DEBUG=1</span></code>.</p>
</section>
<section id="enable-garbage-collection-gc-nvtx-markers">
<h3>Enable garbage collection (GC) NVTX markers<a class="headerlink" href="#enable-garbage-collection-gc-nvtx-markers" title="Link to this heading">#</a></h3>
<p>Set environment variable <code class="docutils literal notranslate"><span class="pre">TLLM_PROFILE_RECORD_GC=1</span></code>.</p>
</section>
<section id="enable-gil-information-in-nvtx-markers">
<h3>Enable GIL information in NVTX markers<a class="headerlink" href="#enable-gil-information-in-nvtx-markers" title="Link to this heading">#</a></h3>
<p>Append “python-gil” to Nsys “-t” option.</p>
</section>
</section>
<section id="coordinating-with-pytorch-profiler-pytorch-workflow-only">
<h2>Coordinating with PyTorch profiler (PyTorch workflow only)<a class="headerlink" href="#coordinating-with-pytorch-profiler-pytorch-workflow-only" title="Link to this heading">#</a></h2>
<section id="collect-pytorch-profiler-results">
<h3>Collect PyTorch profiler results<a class="headerlink" href="#collect-pytorch-profiler-results" title="Link to this heading">#</a></h3>
<ol class="arabic simple">
<li><p>Set environment variable <code class="docutils literal notranslate"><span class="pre">TLLM_PROFILE_START_STOP=A-B</span></code> to specify the range of the iterations to be collected.</p></li>
<li><p>Set environment variable <code class="docutils literal notranslate"><span class="pre">TLLM_TORCH_PROFILE_TRACE=&lt;path&gt;</span></code>, and the results will be saved to <code class="docutils literal notranslate"><span class="pre">&lt;path&gt;</span></code>.</p></li>
</ol>
</section>
<section id="visualize-the-pytorch-profiler-results">
<h3>Visualize the PyTorch profiler results<a class="headerlink" href="#visualize-the-pytorch-profiler-results" title="Link to this heading">#</a></h3>
<p>Use <a class="reference external" href="chrome://tracing/">chrome://tracing/</a> to inspect the saved profile.</p>
</section>
</section>
<section id="examples">
<h2>Examples<a class="headerlink" href="#examples" title="Link to this heading">#</a></h2>
<p>Consult the Nsight Systems User Guide for full overview of MPI-related options.</p>
<section id="profiling-specific-iterations-on-a-trtllm-bench-trtllm-serve-run">
<h3>Profiling specific iterations on a trtllm-bench/trtllm-serve run<a class="headerlink" href="#profiling-specific-iterations-on-a-trtllm-bench-trtllm-serve-run" title="Link to this heading">#</a></h3>
<p>Say we want to profile iterations 100 to 150 on a trtllm-bench/trtllm-serve run, we want to collect as much information as possible for debugging, such as GIL, debugging NVTX markers, etc:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="ch">#!/bin/bash</span>
<span class="c1"># Prepare dataset for the benchmark</span>
python3<span class="w"> </span>benchmarks/cpp/prepare_dataset.py<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--tokenizer<span class="o">=</span><span class="si">${</span><span class="nv">MODEL_PATH</span><span class="si">}</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--stdout<span class="w"> </span>token-norm-dist<span class="w"> </span>--num-requests<span class="o">=</span><span class="si">${</span><span class="nv">NUM_SAMPLES</span><span class="si">}</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--input-mean<span class="o">=</span><span class="m">1000</span><span class="w"> </span>--output-mean<span class="o">=</span><span class="m">1000</span><span class="w"> </span>--input-stdev<span class="o">=</span><span class="m">0</span><span class="w"> </span>--output-stdev<span class="o">=</span><span class="m">0</span><span class="w"> </span>&gt;<span class="w"> </span>/tmp/dataset.txt
<span class="c1"># Benchmark and profile</span>
<span class="nv">TLLM_PROFILE_START_STOP</span><span class="o">=</span><span class="m">100</span>-150<span class="w"> </span>nsys<span class="w"> </span>profile<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>-o<span class="w"> </span>trace<span class="w"> </span>-f<span class="w"> </span><span class="nb">true</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>-t<span class="w"> </span><span class="s1">&#39;cuda,nvtx,python-gil&#39;</span><span class="w"> </span>-c<span class="w"> </span>cudaProfilerApi<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--cuda-graph-trace<span class="w"> </span>node<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>-e<span class="w"> </span><span class="nv">TLLM_PROFILE_RECORD_GC</span><span class="o">=</span><span class="m">1</span>,TLLM_LLMAPI_ENABLE_NVTX<span class="o">=</span><span class="m">1</span>,TLLM_TORCH_PROFILE_TRACE<span class="o">=</span>trace.json<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--trace-fork-before-exec<span class="o">=</span><span class="nb">true</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>trtllm-bench<span class="w"> </span><span class="se">\ </span><span class="c1"># or trtllm-serve command</span>
<span class="w"> </span>--model<span class="w"> </span>deepseek-ai/DeepSeek-V3<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--model_path<span class="w"> </span><span class="si">${</span><span class="nv">MODEL_PATH</span><span class="si">}</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>throughput<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--dataset<span class="w"> </span>/tmp/dataset.txt<span class="w"> </span>--warmup<span class="w"> </span><span class="m">0</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--streaming
</pre></div>
</div>
<p>The Nsight Systems reports will be saved to <code class="docutils literal notranslate"><span class="pre">trace.nsys-rep</span></code>. Use NVIDIA Nsight Systems application to open it.</p>
<p>The PyTorch profiler results will be saved to <code class="docutils literal notranslate"><span class="pre">trace.json</span></code>. Use <a class="reference external" href="chrome://tracing/">chrome://tracing/</a> to inspect the saved profile.</p>
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