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TensorRT-LLMs/installation/build-from-source-linux.html
Kaiyu Xie 178a52e539 Update GitHub pages in root to v1.2.0rc5
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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 current active has-children"><a class="reference internal" href="index.html">Installation</a><details open="open"><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="current">
<li class="toctree-l2"><a class="reference internal" href="containers.html">Pre-built release container images on NGC</a></li>
<li class="toctree-l2"><a class="reference internal" href="linux.html">Installing on Linux via <code class="docutils literal notranslate"><span class="pre">pip</span></code></a></li>
<li class="toctree-l2 current active"><a class="current reference internal" href="#">Building from Source Code on Linux</a></li>
</ul>
</details></li>
</ul>
<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>
<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/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-on-trtllm.html">Deployment Guide for Qwen3 on TensorRT LLM - Blackwell &amp; Hopper 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>
<li class="toctree-l2"><a class="reference internal" href="../deployment-guide/deployment-guide-for-kimi-k2-thinking-on-trtllm.html">Deployment Guide for Kimi K2 Thinking on TensorRT LLM - Blackwell</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>
</ul>
<p aria-level="2" class="caption" role="heading"><span class="caption-text">CLI Reference</span></p>
<ul class="nav bd-sidenav">
<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>
<li class="toctree-l2"><a class="reference internal" href="../commands/trtllm-serve/trtllm-serve.html">trtllm-serve</a></li>
<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>
</details></li>
</ul>
<p aria-level="2" class="caption" role="heading"><span class="caption-text">API Reference</span></p>
<ul class="nav bd-sidenav">
<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</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/guided-decoding.html">Guided Decoding</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>
<li class="toctree-l1"><a class="reference internal" href="../features/helix.html">Helix Parallelism</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/kv-cache-connector.html">KV Cache Connector</a></li>
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<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="../developer-guide/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>
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<section class="tex2jax_ignore mathjax_ignore" id="building-from-source-code-on-linux">
<span id="build-from-source-linux"></span><h1>Building from Source Code on Linux<a class="headerlink" href="#building-from-source-code-on-linux" title="Link to this heading">#</a></h1>
<p>This document provides instructions for building TensorRT LLM from source code on Linux. Building from source is recommended for achieving optimal performance, enabling debugging capabilities, or when you need a different <a class="reference external" href="https://gcc.gnu.org/onlinedocs/libstdc++/manual/using_dual_abi.html">GNU CXX11 ABI</a> configuration than what is available in the pre-built TensorRT LLM wheel on PyPI. Note that the current pre-built TensorRT LLM wheel on PyPI is linked against PyTorch 2.7.0 and subsequent versions, which uses the new CXX11 ABI.</p>
<section id="prerequisites">
<h2>Prerequisites<a class="headerlink" href="#prerequisites" title="Link to this heading">#</a></h2>
<p>Use <a class="reference external" href="https://www.docker.com">Docker</a> to build and run TensorRT LLM. Instructions to install an environment to run Docker containers for the NVIDIA platform can be found <a class="reference external" href="https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html">here</a>.</p>
<p>If you intend to build any TensortRT-LLM artifacts, such as any of the container images (note that there exist pre-built <a class="reference internal" href="#build-from-source-tip-develop-container">develop</a> and <a class="reference internal" href="#build-from-source-tip-release-container">release</a> container images in NGC), or the TensorRT LLM Python wheel, you first need to clone the TensorRT LLM repository:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># TensorRT LLM uses git-lfs, which needs to be installed in advance.</span>
apt-get<span class="w"> </span>update<span class="w"> </span><span class="o">&amp;&amp;</span><span class="w"> </span>apt-get<span class="w"> </span>-y<span class="w"> </span>install<span class="w"> </span>git<span class="w"> </span>git-lfs
git<span class="w"> </span>lfs<span class="w"> </span>install
git<span class="w"> </span>clone<span class="w"> </span>https://github.com/NVIDIA/TensorRT-LLM.git
<span class="nb">cd</span><span class="w"> </span>TensorRT-LLM
git<span class="w"> </span>submodule<span class="w"> </span>update<span class="w"> </span>--init<span class="w"> </span>--recursive
git<span class="w"> </span>lfs<span class="w"> </span>pull
</pre></div>
</div>
</section>
<section id="building-a-tensorrt-llm-docker-image">
<h2>Building a TensorRT LLM Docker Image<a class="headerlink" href="#building-a-tensorrt-llm-docker-image" title="Link to this heading">#</a></h2>
<p>There are two options to create a TensorRT LLM Docker image. The approximate disk space required to build the image is 63 GB.</p>
<section id="option-1-build-tensorrt-llm-in-one-step">
<h3>Option 1: Build TensorRT LLM in One Step<a class="headerlink" href="#option-1-build-tensorrt-llm-in-one-step" title="Link to this heading">#</a></h3>
<div class="admonition tip" id="build-from-source-tip-release-container">
<p class="admonition-title">Tip</p>
<p>If you just want to run TensorRT LLM, you can instead <a class="reference internal" href="containers.html#containers"><span class="std std-ref">use the pre-built TensorRT LLM Release container images</span></a>.</p>
</div>
<p>TensorRT LLM contains a simple command to create a Docker image. Note that if you plan to develop on TensorRT LLM, we recommend using <a class="reference internal" href="#option-2-build-tensorrt-llm-step-by-step">Option 2: Build TensorRT LLM Step-By-Step</a>.</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>make<span class="w"> </span>-C<span class="w"> </span>docker<span class="w"> </span>release_build
</pre></div>
</div>
<p>You can add the <code class="docutils literal notranslate"><span class="pre">CUDA_ARCHS=&quot;&lt;list</span> <span class="pre">of</span> <span class="pre">architectures</span> <span class="pre">in</span> <span class="pre">CMake</span> <span class="pre">format&gt;&quot;</span></code> optional argument to specify which architectures should be supported by TensorRT LLM. It restricts the supported GPU architectures but helps reduce compilation time:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># Restrict the compilation to Ada and Hopper architectures.</span>
make<span class="w"> </span>-C<span class="w"> </span>docker<span class="w"> </span>release_build<span class="w"> </span><span class="nv">CUDA_ARCHS</span><span class="o">=</span><span class="s2">&quot;89-real;90-real&quot;</span>
</pre></div>
</div>
<p>After the image is built, the Docker container can be run.</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>make<span class="w"> </span>-C<span class="w"> </span>docker<span class="w"> </span>release_run
</pre></div>
</div>
<p>The <code class="docutils literal notranslate"><span class="pre">make</span></code> command supports the <code class="docutils literal notranslate"><span class="pre">LOCAL_USER=1</span></code> argument to switch to the local user account instead of <code class="docutils literal notranslate"><span class="pre">root</span></code> inside the container. The examples of TensorRT LLM are installed in the <code class="docutils literal notranslate"><span class="pre">/app/tensorrt_llm/examples</span></code> directory.</p>
<p>Since TensorRT LLM has been built and installed, you can skip the remaining steps.</p>
</section>
<section id="option-2-container-for-building-tensorrt-llm-step-by-step">
<span id="option-2-build-tensorrt-llm-step-by-step"></span><h3>Option 2: Container for building TensorRT LLM Step-by-Step<a class="headerlink" href="#option-2-container-for-building-tensorrt-llm-step-by-step" title="Link to this heading">#</a></h3>
<p>If you are looking for more flexibility, TensorRT LLM has commands to create and run a development container in which TensorRT LLM can be built.</p>
<div class="admonition tip" id="build-from-source-tip-develop-container">
<p class="admonition-title">Tip</p>
<p>As an alternative to building the container image following the instructions below,
you can pull a pre-built <a class="reference external" href="https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tensorrt-llm/containers/devel">TensorRT LLM Develop container image</a> from NGC (see <a class="reference internal" href="containers.html#containers"><span class="std std-ref">here</span></a> for information on container tags).
Follow the linked catalog entry to enter a new container based on the pre-built container image, with the TensorRT source repository mounted into it. You can then skip this section and continue straight to <a class="reference internal" href="#build-tensorrt-llm">building TensorRT LLM</a>.</p>
</div>
<p><strong>On systems with GNU <code class="docutils literal notranslate"><span class="pre">make</span></code></strong></p>
<ol class="arabic">
<li><p>Create a Docker image for development. The image will be tagged locally with <code class="docutils literal notranslate"><span class="pre">tensorrt_llm/devel:latest</span></code>.</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>make<span class="w"> </span>-C<span class="w"> </span>docker<span class="w"> </span>build
</pre></div>
</div>
</li>
<li><p>Run the container.</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>make<span class="w"> </span>-C<span class="w"> </span>docker<span class="w"> </span>run
</pre></div>
</div>
<p>If you prefer to work with your own user account in that container, instead of <code class="docutils literal notranslate"><span class="pre">root</span></code>, add the <code class="docutils literal notranslate"><span class="pre">LOCAL_USER=1</span></code> option.</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>make<span class="w"> </span>-C<span class="w"> </span>docker<span class="w"> </span>run<span class="w"> </span><span class="nv">LOCAL_USER</span><span class="o">=</span><span class="m">1</span>
</pre></div>
</div>
</li>
</ol>
<p>If you wish to use enroot instead of docker, then you can build a sqsh file that has the identical environment as the development image <code class="docutils literal notranslate"><span class="pre">tensorrt_llm/devel:latest</span></code> as follows.</p>
<ol class="arabic">
<li><p>Allocate a compute node:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>salloc<span class="w"> </span>--nodes<span class="o">=</span><span class="m">1</span>
</pre></div>
</div>
</li>
<li><p>Create a sqsh file with essential TensorRT LLM dependencies installed</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># Using default sqsh filename (enroot/tensorrt_llm.devel.sqsh)</span>
make<span class="w"> </span>-C<span class="w"> </span>enroot<span class="w"> </span>build_sqsh
<span class="c1"># Or specify a custom path (optional)</span>
make<span class="w"> </span>-C<span class="w"> </span>enroot<span class="w"> </span>build_sqsh<span class="w"> </span><span class="nv">SQSH_PATH</span><span class="o">=</span>/path/to/dev_trtllm_image.sqsh
</pre></div>
</div>
</li>
<li><p>Once this squash file is ready, you can follow the steps under <a class="reference internal" href="#build-tensorrt-llm">Build TensorRT LLM</a>by launching an enroot sandbox from <code class="docutils literal notranslate"><span class="pre">dev_trtllm_image.sqsh</span></code>. To do this, proceed as follows:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nb">export</span><span class="w"> </span><span class="nv">SQSH_PATH</span><span class="o">=</span>/path/to/dev_trtllm_image.sqsh
<span class="c1"># Start a pseudo terminal for interactive session</span>
make<span class="w"> </span>-C<span class="w"> </span>enroot<span class="w"> </span>run_sqsh
<span class="c1"># Or, you could run commands directly</span>
make<span class="w"> </span>-C<span class="w"> </span>enroot<span class="w"> </span>run_sqsh<span class="w"> </span><span class="nv">RUN_CMD</span><span class="o">=</span><span class="s2">&quot;python3 scripts/build_wheel.py&quot;</span>
</pre></div>
</div>
</li>
</ol>
<p><strong>On systems without GNU <code class="docutils literal notranslate"><span class="pre">make</span></code></strong></p>
<ol class="arabic">
<li><p>Create a Docker image for development.</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>docker<span class="w"> </span>build<span class="w"> </span>--pull<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--target<span class="w"> </span>devel<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--file<span class="w"> </span>docker/Dockerfile.multi<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--tag<span class="w"> </span>tensorrt_llm/devel:latest<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>.
</pre></div>
</div>
</li>
<li><p>Run the container.</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>docker<span class="w"> </span>run<span class="w"> </span>--rm<span class="w"> </span>-it<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--ipc<span class="o">=</span>host<span class="w"> </span>--ulimit<span class="w"> </span><span class="nv">memlock</span><span class="o">=</span>-1<span class="w"> </span>--ulimit<span class="w"> </span><span class="nv">stack</span><span class="o">=</span><span class="m">67108864</span><span class="w"> </span>--gpus<span class="o">=</span>all<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--volume<span class="w"> </span><span class="si">${</span><span class="nv">PWD</span><span class="si">}</span>:/code/tensorrt_llm<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>--workdir<span class="w"> </span>/code/tensorrt_llm<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>tensorrt_llm/devel:latest
</pre></div>
</div>
<p>Note: please make sure to set <code class="docutils literal notranslate"><span class="pre">--ipc=host</span></code> as a docker run argument to avoid <code class="docutils literal notranslate"><span class="pre">Bus</span> <span class="pre">error</span> <span class="pre">(core</span> <span class="pre">dumped)</span></code>.</p>
</li>
</ol>
<p>Once inside the container, follow the next steps to build TensorRT LLM from source.</p>
</section>
<section id="advanced-topics">
<h3>Advanced topics<a class="headerlink" href="#advanced-topics" title="Link to this heading">#</a></h3>
<p>For more information on building and running various TensorRT LLM container images,
check <a class="github reference external" href="https://github.com/NVIDIA/TensorRT-LLM/tree/main/docker">NVIDIA/TensorRT-LLM</a>.</p>
</section>
</section>
<section id="build-tensorrt-llm">
<h2>Build TensorRT LLM<a class="headerlink" href="#build-tensorrt-llm" title="Link to this heading">#</a></h2>
<section id="option-1-full-build-with-c-compilation">
<h3>Option 1: Full Build with C++ Compilation<a class="headerlink" href="#option-1-full-build-with-c-compilation" title="Link to this heading">#</a></h3>
<p>The following command compiles the C++ code and packages the compiled libraries along with the Python files into a wheel. When developing C++ code, you need this full build command to apply your code changes.</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># To build the TensorRT LLM code.</span>
python3<span class="w"> </span>./scripts/build_wheel.py
</pre></div>
</div>
<p>Once the wheel is built, install it by:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>./build/tensorrt_llm*.whl
</pre></div>
</div>
<p>Alternatively, you can use editable installation, which is convenient if you also develop Python code.</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>-e<span class="w"> </span>.
</pre></div>
</div>
<p>By default, <code class="docutils literal notranslate"><span class="pre">build_wheel.py</span></code> enables incremental builds. To clean the build
directory, add the <code class="docutils literal notranslate"><span class="pre">--clean</span></code> option:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>python3<span class="w"> </span>./scripts/build_wheel.py<span class="w"> </span>--clean
</pre></div>
</div>
<p>It is possible to restrict the compilation of TensorRT LLM to specific CUDA
architectures. For that purpose, the <code class="docutils literal notranslate"><span class="pre">build_wheel.py</span></code> script accepts a
semicolon separated list of CUDA architecture as shown in the following
example:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># Build TensorRT LLM for Ampere.</span>
python3<span class="w"> </span>./scripts/build_wheel.py<span class="w"> </span>--cuda_architectures<span class="w"> </span><span class="s2">&quot;80-real;86-real&quot;</span>
</pre></div>
</div>
<p>To use the C++ benchmark scripts under <a class="reference external" href="https://github.com/NVIDIA/TensorRT-LLM/tree/e4c7078/benchmarks/cpp/">benchmark/cpp</a>, for example <code class="docutils literal notranslate"><span class="pre">gptManagerBenchmark.cpp</span></code>, add the <code class="docutils literal notranslate"><span class="pre">--benchmarks</span></code> option:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>python3<span class="w"> </span>./scripts/build_wheel.py<span class="w"> </span>--benchmarks
</pre></div>
</div>
<p>Refer to the <a class="reference internal" href="../legacy/reference/support-matrix.html#support-matrix-hardware"><span class="std std-ref">Hardware</span></a> section for a list of architectures.</p>
<section id="building-the-python-bindings-for-the-c-runtime">
<h4>Building the Python Bindings for the C++ Runtime<a class="headerlink" href="#building-the-python-bindings-for-the-c-runtime" title="Link to this heading">#</a></h4>
<p>The C++ Runtime can be exposed to Python via bindings. This feature can be turned on through the default build options.</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>python3<span class="w"> </span>./scripts/build_wheel.py
</pre></div>
</div>
<p>After installing, the resulting wheel as described above, the C++ Runtime bindings will be available in
the <code class="docutils literal notranslate"><span class="pre">tensorrt_llm.bindings</span></code> package. Running <code class="docutils literal notranslate"><span class="pre">help</span></code> on this package in a Python interpreter will provide on overview of the
relevant classes. The associated unit tests should also be consulted for understanding the API.</p>
<p>This feature will not be enabled when <a class="reference internal" href="#link-with-the-tensorrt-llm-c-runtime"><code class="docutils literal notranslate"><span class="pre">building</span> <span class="pre">only</span> <span class="pre">the</span> <span class="pre">C++</span> <span class="pre">runtime</span></code></a>.</p>
</section>
<section id="linking-with-the-tensorrt-llm-c-runtime">
<span id="link-with-the-tensorrt-llm-c-runtime"></span><h4>Linking with the TensorRT LLM C++ Runtime<a class="headerlink" href="#linking-with-the-tensorrt-llm-c-runtime" title="Link to this heading">#</a></h4>
<p>The <code class="docutils literal notranslate"><span class="pre">build_wheel.py</span></code> script will also compile the library containing the C++ runtime of TensorRT LLM. If Python support and <code class="docutils literal notranslate"><span class="pre">torch</span></code> modules are not required, the script provides the option <code class="docutils literal notranslate"><span class="pre">--cpp_only</span></code> which restricts the build to the C++ runtime only.</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>python3<span class="w"> </span>./scripts/build_wheel.py<span class="w"> </span>--cuda_architectures<span class="w"> </span><span class="s2">&quot;80-real;86-real&quot;</span><span class="w"> </span>--cpp_only<span class="w"> </span>--clean
</pre></div>
</div>
<p>This is particularly useful for avoiding linking issues that may arise with older versions of <code class="docutils literal notranslate"><span class="pre">torch</span></code> (prior to 2.7.0) due to the <a class="reference external" href="https://gcc.gnu.org/onlinedocs/libstdc++/manual/using_dual_abi.html">Dual ABI support in GCC</a>. The <code class="docutils literal notranslate"><span class="pre">--clean</span></code> option removes the build directory before starting a new build. By default, TensorRT LLM uses <code class="docutils literal notranslate"><span class="pre">cpp/build</span></code> as the build directory, but you can specify a different location with the <code class="docutils literal notranslate"><span class="pre">--build_dir</span></code> option. For a complete list of available build options, run <code class="docutils literal notranslate"><span class="pre">python3</span> <span class="pre">./scripts/build_wheel.py</span> <span class="pre">--help</span></code>.</p>
<p>The shared library can be found in the following location:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>cpp/build/tensorrt_llm/libtensorrt_llm.so
</pre></div>
</div>
<p>In addition, link against the library containing the LLM plugins for TensorRT.</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>cpp/build/tensorrt_llm/plugins/libnvinfer_plugin_tensorrt_llm.so
</pre></div>
</div>
</section>
<section id="supported-c-header-files">
<h4>Supported C++ Header Files<a class="headerlink" href="#supported-c-header-files" title="Link to this heading">#</a></h4>
<p>When using TensorRT LLM, you need to add the <code class="docutils literal notranslate"><span class="pre">cpp</span></code> and <code class="docutils literal notranslate"><span class="pre">cpp/include</span></code> directories to the project’s include paths. Only header files contained in <code class="docutils literal notranslate"><span class="pre">cpp/include</span></code> are part of the supported API and may be directly included. Other headers contained under <code class="docutils literal notranslate"><span class="pre">cpp</span></code> should not be included directly since they might change in future versions.</p>
</section>
</section>
<section id="option-2-python-only-build-without-c-compilation">
<h3>Option 2: Python-Only Build without C++ Compilation<a class="headerlink" href="#option-2-python-only-build-without-c-compilation" title="Link to this heading">#</a></h3>
<p>If you only need to modify Python code, it is possible to package and install TensorRT LLM without compilation.</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># Package TensorRT LLM wheel.</span>
<span class="nv">TRTLLM_USE_PRECOMPILED</span><span class="o">=</span><span class="m">1</span><span class="w"> </span>pip<span class="w"> </span>wheel<span class="w"> </span>.<span class="w"> </span>--no-deps<span class="w"> </span>--wheel-dir<span class="w"> </span>./build
<span class="c1"># Install TensorRT LLM wheel.</span>
pip<span class="w"> </span>install<span class="w"> </span>./build/tensorrt_llm*.whl
</pre></div>
</div>
<p>Alternatively, you can use editable installation for convenience during Python development.</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nv">TRTLLM_USE_PRECOMPILED</span><span class="o">=</span><span class="m">1</span><span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>-e<span class="w"> </span>.
</pre></div>
</div>
<p>Setting <code class="docutils literal notranslate"><span class="pre">TRTLLM_USE_PRECOMPILED=1</span></code> enables downloading a prebuilt wheel of the version specified in <code class="docutils literal notranslate"><span class="pre">tensorrt_llm/version.py</span></code>, extracting compiled libraries into your current directory, thus skipping C++ compilation. This version can be overridden by specifying <code class="docutils literal notranslate"><span class="pre">TRTLLM_USE_PRECOMPILED=x.y.z</span></code>.</p>
<p>You can specify a custom URL or local path for downloading using <code class="docutils literal notranslate"><span class="pre">TRTLLM_PRECOMPILED_LOCATION</span></code>. For example, to use version 0.16.0 from PyPI:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="nv">TRTLLM_PRECOMPILED_LOCATION</span><span class="o">=</span>https://pypi.nvidia.com/tensorrt-llm/tensorrt_llm-0.16.0-cp312-cp312-linux_x86_64.whl<span class="w"> </span>pip<span class="w"> </span>install<span class="w"> </span>-e<span class="w"> </span>.
</pre></div>
</div>
<section id="known-limitations">
<h4>Known Limitations<a class="headerlink" href="#known-limitations" title="Link to this heading">#</a></h4>
<p>When using <code class="docutils literal notranslate"><span class="pre">TRTLLM_PRECOMPILED_LOCATION</span></code>, ensure that your wheel is compiled based on the same version of C++ code as your current directory; any discrepancies may lead to compatibility issues.</p>
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