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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>
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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>
<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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<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>
<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_sparse_attention.html">Sparse Attention</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_kv_cache_offloading.html">KV Cache Offloading</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>
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<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/deployment-guide-for-deepseek-r1-on-trtllm.html">Deployment Guide for DeepSeek R1 on TensorRT LLM - Blackwell &amp; 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 &amp; 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 &amp; 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 &amp; Hopper Hardware</a></li>
</ul>
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<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/adding-new-model.html">Adding a New Model</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../commands/trtllm-bench.html">trtllm-bench</a></li>
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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>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Features</span></p>
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<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</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/additional-outputs.html">Additional Outputs</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>
<li class="toctree-l1"><a class="reference internal" href="../features/ray-orchestrator.html">Ray Orchestrator (Prototype)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/torch_compile_and_piecewise_cuda_graph.html">Torch Compile &amp; Piecewise CUDA Graph</a></li>
</ul>
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Developer Guide</span></p>
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<li class="toctree-l1"><a class="reference internal" href="overview.html">Architecture Overview</a></li>
<li class="toctree-l1 current active"><a class="current reference internal" href="#">Performance Analysis</a></li>
<li class="toctree-l1"><a class="reference internal" href="perf-benchmarking.html">TensorRT LLM Benchmarking</a></li>
<li class="toctree-l1"><a class="reference internal" href="ci-overview.html">Continuous Integration Overview</a></li>
<li class="toctree-l1"><a class="reference internal" href="dev-containers.html">Using Dev Containers</a></li>
<li class="toctree-l1"><a class="reference internal" href="api-change.html">LLM API Change Guide</a></li>
<li class="toctree-l1"><a class="reference internal" href="kv-transfer.html">Introduction to KV Cache Transmission</a></li>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Blogs</span></p>
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<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/blog12_Combining_Guided_Decoding_and_Speculative_Decoding.html">Combining Guided Decoding and Speculative Decoding: Making CPU and GPU Cooperate Seamlessly</a></li>
<li class="toctree-l1"><a class="reference internal" href="../blogs/tech_blog/blog13_Inference_Time_Compute_Implementation_in_TensorRT-LLM.html">Inference Time Compute Implementation in TensorRT LLM</a></li>
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<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>
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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/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 class="tex2jax_ignore mathjax_ignore" 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, NVIDIA has 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:</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 ensure 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 internal" href="#chrome://tracing/"><span class="xref myst">chrome://tracing/</span></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 <code class="docutils literal notranslate"><span class="pre">trtllm-bench</span></code>/<code class="docutils literal notranslate"><span class="pre">trtllm-serve</span></code> 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 <code class="docutils literal notranslate"><span class="pre">trtllm-bench</span></code>/<code class="docutils literal notranslate"><span class="pre">trtllm-serve</span></code> 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>--backend<span class="w"> </span>pytorch<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 internal" href="#chrome://tracing/"><span class="xref myst">chrome://tracing/</span></a> to inspect the saved profile.</p>
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