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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"><a class="reference internal" href="../key-features.html">Key Features</a></li>
<li class="toctree-l1"><a class="reference internal" href="../torch.html">PyTorch Backend</a></li>
<li class="toctree-l1"><a class="reference internal" href="../release-notes.html">Release Notes</a></li>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Installation</span></p>
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<li class="toctree-l1"><a class="reference internal" href="containers.html">Pre-built release container images on NGC</a></li>
<li class="toctree-l1 current active"><a class="current reference internal" href="#">Installing on Linux via <code class="docutils literal notranslate"><span class="pre">pip</span></code></a></li>
<li class="toctree-l1"><a class="reference internal" href="build-from-source-linux.html">Building from Source Code on Linux</a></li>
</ul>
<p aria-level="2" class="caption" role="heading"><span class="caption-text">LLM API</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">Examples</span></p>
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<li class="toctree-l1 has-children"><a class="reference internal" href="../examples/index.html">LLM Examples Introduction</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul class="simple">
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<li class="toctree-l1"><a class="reference internal" href="../examples/customization.html">LLM Common Customizations</a></li>
<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_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>
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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_completion_client_for_lora.html">Openai Completion Client For Lora</a></li>
</ul>
</details></li>
</ul>
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Model Definition API</span></p>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Architecture</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../architecture/overview.html">TensorRT-LLM Architecture</a></li>
<li class="toctree-l1"><a class="reference internal" href="../architecture/core-concepts.html">Model Definition</a></li>
<li class="toctree-l1"><a class="reference internal" href="../architecture/checkpoint.html">TensorRT-LLM Checkpoint</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../architecture/add-model.html">Adding a Model</a></li>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Advanced</span></p>
<ul class="nav bd-sidenav">
<li class="toctree-l1"><a class="reference internal" href="../advanced/gpt-attention.html">Multi-Head, Multi-Query, and Group-Query Attention</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/gpt-runtime.html">C++ GPT Runtime</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/executor.html">Executor API</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/graph-rewriting.html">Graph Rewriting Module</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/lora.html">Run gpt-2b + LoRA using Executor / cpp runtime</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/expert-parallelism.html">Expert Parallelism in TensorRT-LLM</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/kv-cache-management.html">KV Cache Management: Pools, Blocks, and Events</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/kv-cache-reuse.html">KV cache reuse</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/speculative-decoding.html">Speculative Sampling</a></li>
<li class="toctree-l1"><a class="reference internal" href="../advanced/disaggregated-service.html">Disaggregated-Service (experimental)</a></li>
</ul>
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Performance</span></p>
<ul class="nav bd-sidenav">
<li class="toctree-l1"><a class="reference internal" href="../performance/perf-overview.html">Overview</a></li>
<li class="toctree-l1"><a class="reference internal" href="../performance/perf-benchmarking.html">Benchmarking</a></li>
<li class="toctree-l1 has-children"><a class="reference internal" href="../performance/performance-tuning-guide/index.html">Performance Tuning Guide</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="../performance/performance-tuning-guide/benchmarking-default-performance.html">Benchmarking Default Performance</a></li>
<li class="toctree-l2"><a class="reference internal" href="../performance/performance-tuning-guide/useful-build-time-flags.html">Useful Build-Time Flags</a></li>
<li class="toctree-l2"><a class="reference internal" href="../performance/performance-tuning-guide/tuning-max-batch-size-and-max-num-tokens.html">Tuning Max Batch Size and Max Num Tokens</a></li>
<li class="toctree-l2"><a class="reference internal" href="../performance/performance-tuning-guide/deciding-model-sharding-strategy.html">Deciding Model Sharding Strategy</a></li>
<li class="toctree-l2"><a class="reference internal" href="../performance/performance-tuning-guide/fp8-quantization.html">FP8 Quantization</a></li>
<li class="toctree-l2"><a class="reference internal" href="../performance/performance-tuning-guide/useful-runtime-flags.html">Useful Runtime Options</a></li>
</ul>
</details></li>
<li class="toctree-l1"><a class="reference internal" href="../performance/perf-analysis.html">Performance Analysis</a></li>
</ul>
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Reference</span></p>
<ul class="nav bd-sidenav">
<li class="toctree-l1"><a class="reference internal" href="../reference/troubleshooting.html">Troubleshooting</a></li>
<li class="toctree-l1"><a class="reference internal" href="../reference/support-matrix.html">Support Matrix</a></li>
<li class="toctree-l1"><a class="reference internal" href="../reference/precision.html">Numerical Precision</a></li>
<li class="toctree-l1"><a class="reference internal" href="../reference/memory.html">Memory Usage of TensorRT-LLM</a></li>
<li class="toctree-l1"><a class="reference internal" href="../reference/ci-overview.html">Continuous Integration Overview</a></li>
<li class="toctree-l1"><a class="reference internal" href="../reference/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/H100vsA100.html">H100 has 4.6x A100 Performance in TensorRT-LLM, achieving 10,000 tok/s at 100ms to first token</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/Falcon180B-H200.html">Falcon-180B on a single H200 GPU with INT4 AWQ, and 6.7x faster Llama-70B over A100</a></li>
<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>
<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/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="breadcrumb-item active" aria-current="page"><span class="ellipsis">Installing on Linux via <code class="docutils literal notranslate"><span class="pre">pip</span></code></span></li>
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<section id="installing-on-linux-via-pip">
<span id="linux"></span><h1>Installing on Linux via <code class="docutils literal notranslate"><span class="pre">pip</span></code><a class="headerlink" href="#installing-on-linux-via-pip" title="Link to this heading">#</a></h1>
<ol class="arabic">
<li><p>Install TensorRT-LLM (tested on Ubuntu 24.04).</p>
<p class="rubric" id="install-prerequisites">Install prerequisites</p>
<p>Before the pre-built Python wheel can be installed via <code class="docutils literal notranslate"><span class="pre">pip</span></code>, a few
prerequisites must be put into place:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># Optional step: Only required for Blackwell and Grace Hopper</span>
pip3<span class="w"> </span>install<span class="w"> </span><span class="nv">torch</span><span class="o">==</span><span class="m">2</span>.7.1<span class="w"> </span>torchvision<span class="w"> </span>torchaudio<span class="w"> </span>--index-url<span class="w"> </span>https://download.pytorch.org/whl/cu128
sudo<span class="w"> </span>apt-get<span class="w"> </span>-y<span class="w"> </span>install<span class="w"> </span>libopenmpi-dev
</pre></div>
</div>
<p>PyTorch CUDA 12.8 package is required for supporting NVIDIA Blackwell and Grace Hopper GPUs. On prior GPUs, this extra installation is not required.</p>
<div class="admonition tip">
<p class="admonition-title">Tip</p>
<p>Instead of manually installing the preqrequisites as described
above, it is also possible to use the pre-built <a class="reference external" href="https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tensorrt-llm/containers/devel">TensorRT-LLM Develop container
image hosted on NGC</a>
(see <a class="reference internal" href="containers.html"><span class="doc std std-doc">here</span></a> for information on container tags).</p>
</div>
<p class="rubric" id="install-pre-built-tensorrt-llm-wheel">Install pre-built TensorRT-LLM wheel</p>
<p>Once all prerequisites are in place, TensorRT-LLM can be installed as follows:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip3<span class="w"> </span>install<span class="w"> </span>--upgrade<span class="w"> </span>pip<span class="w"> </span>setuptools<span class="w"> </span><span class="o">&amp;&amp;</span><span class="w"> </span>pip3<span class="w"> </span>install<span class="w"> </span>tensorrt_llm
</pre></div>
</div>
</li>
<li><p>Sanity check the installation by running the following in Python (tested on Python 3.12):</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="linenos"> 1</span><span class="kn">from</span><span class="w"> </span><span class="nn">tensorrt_llm</span><span class="w"> </span><span class="kn">import</span> <span class="n">SamplingParams</span>
<span class="linenos"> 2</span><span class="kn">from</span><span class="w"> </span><span class="nn">tensorrt_llm._tensorrt_engine</span><span class="w"> </span><span class="kn">import</span> <span class="n">LLM</span>
<span class="linenos"> 3</span>
<span class="linenos"> 4</span>
<span class="linenos"> 5</span><span class="k">def</span><span class="w"> </span><span class="nf">main</span><span class="p">():</span>
<span class="linenos"> 6</span>
<span class="linenos"> 7</span> <span class="n">prompts</span> <span class="o">=</span> <span class="p">[</span>
<span class="linenos"> 8</span> <span class="s2">&quot;Hello, my name is&quot;</span><span class="p">,</span>
<span class="linenos"> 9</span> <span class="s2">&quot;The president of the United States is&quot;</span><span class="p">,</span>
<span class="linenos">10</span> <span class="s2">&quot;The capital of France is&quot;</span><span class="p">,</span>
<span class="linenos">11</span> <span class="s2">&quot;The future of AI is&quot;</span><span class="p">,</span>
<span class="linenos">12</span> <span class="p">]</span>
<span class="linenos">13</span> <span class="n">sampling_params</span> <span class="o">=</span> <span class="n">SamplingParams</span><span class="p">(</span><span class="n">temperature</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span> <span class="n">top_p</span><span class="o">=</span><span class="mf">0.95</span><span class="p">)</span>
<span class="linenos">14</span>
<span class="linenos">15</span> <span class="n">llm</span> <span class="o">=</span> <span class="n">LLM</span><span class="p">(</span><span class="n">model</span><span class="o">=</span><span class="s2">&quot;TinyLlama/TinyLlama-1.1B-Chat-v1.0&quot;</span><span class="p">)</span>
<span class="linenos">16</span>
<span class="linenos">17</span> <span class="n">outputs</span> <span class="o">=</span> <span class="n">llm</span><span class="o">.</span><span class="n">generate</span><span class="p">(</span><span class="n">prompts</span><span class="p">,</span> <span class="n">sampling_params</span><span class="p">)</span>
<span class="linenos">18</span>
<span class="linenos">19</span> <span class="c1"># Print the outputs.</span>
<span class="linenos">20</span> <span class="k">for</span> <span class="n">output</span> <span class="ow">in</span> <span class="n">outputs</span><span class="p">:</span>
<span class="linenos">21</span> <span class="n">prompt</span> <span class="o">=</span> <span class="n">output</span><span class="o">.</span><span class="n">prompt</span>
<span class="linenos">22</span> <span class="n">generated_text</span> <span class="o">=</span> <span class="n">output</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">text</span>
<span class="linenos">23</span> <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Prompt: </span><span class="si">{</span><span class="n">prompt</span><span class="si">!r}</span><span class="s2">, Generated text: </span><span class="si">{</span><span class="n">generated_text</span><span class="si">!r}</span><span class="s2">&quot;</span><span class="p">)</span>
<span class="linenos">24</span>
<span class="linenos">25</span>
<span class="linenos">26</span><span class="c1"># The entry point of the program need to be protected for spawning processes.</span>
<span class="linenos">27</span><span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
<span class="linenos">28</span> <span class="n">main</span><span class="p">()</span>
</pre></div>
</div>
</li>
</ol>
<p><strong>Known limitations</strong></p>
<p>There are some known limitations when you pip install pre-built TensorRT-LLM wheel package.</p>
<ol class="arabic">
<li><p>MPI in the Slurm environment</p>
<p>If you encounter an error while running TensorRT-LLM in a Slurm-managed cluster, you need to reconfigure the MPI installation to work with Slurm.
The setup methods depends on your slurm configuration, pls check with your admin. This is not a TensorRT-LLM specific, rather a general mpi+slurm issue.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">The</span> <span class="n">application</span> <span class="n">appears</span> <span class="n">to</span> <span class="n">have</span> <span class="n">been</span> <span class="n">direct</span> <span class="n">launched</span> <span class="n">using</span> <span class="s2">&quot;srun&quot;</span><span class="p">,</span>
<span class="n">but</span> <span class="n">OMPI</span> <span class="n">was</span> <span class="ow">not</span> <span class="n">built</span> <span class="k">with</span> <span class="n">SLURM</span> <span class="n">support</span><span class="o">.</span> <span class="n">This</span> <span class="n">usually</span> <span class="n">happens</span>
<span class="n">when</span> <span class="n">OMPI</span> <span class="n">was</span> <span class="ow">not</span> <span class="n">configured</span> <span class="o">--</span><span class="k">with</span><span class="o">-</span><span class="n">slurm</span> <span class="ow">and</span> <span class="n">we</span> <span class="n">weren</span><span class="s1">&#39;t able</span>
<span class="n">to</span> <span class="n">discover</span> <span class="n">a</span> <span class="n">SLURM</span> <span class="n">installation</span> <span class="ow">in</span> <span class="n">the</span> <span class="n">usual</span> <span class="n">places</span><span class="o">.</span>
</pre></div>
</div>
</li>
<li><p>CUDA Toolkit</p>
<p><code class="docutils literal notranslate"><span class="pre">pip</span> <span class="pre">install</span> <span class="pre">tensorrt-llm</span></code> won’t install CUDA toolkit in your system, and the CUDA Toolkit is not required if want to just deploy a TensorRT-LLM engine.
TensorRT-LLM uses the <a class="reference external" href="https://nvidia.github.io/TensorRT-Model-Optimizer/">ModelOpt</a> to quantize a model, while the ModelOpt requires CUDA toolkit to jit compile certain kernels which is not included in the pytorch to do quantization effectively.
Please install CUDA toolkit when you see the following message when running ModelOpt quantization.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="o">/</span><span class="n">usr</span><span class="o">/</span><span class="n">local</span><span class="o">/</span><span class="n">lib</span><span class="o">/</span><span class="n">python3</span><span class="mf">.10</span><span class="o">/</span><span class="n">dist</span><span class="o">-</span><span class="n">packages</span><span class="o">/</span><span class="n">modelopt</span><span class="o">/</span><span class="n">torch</span><span class="o">/</span><span class="n">utils</span><span class="o">/</span><span class="n">cpp_extension</span><span class="o">.</span><span class="n">py</span><span class="p">:</span><span class="mi">65</span><span class="p">:</span>
<span class="ne">UserWarning</span><span class="p">:</span> <span class="n">CUDA_HOME</span> <span class="n">environment</span> <span class="n">variable</span> <span class="ow">is</span> <span class="ow">not</span> <span class="nb">set</span><span class="o">.</span> <span class="n">Please</span> <span class="nb">set</span> <span class="n">it</span> <span class="n">to</span> <span class="n">your</span> <span class="n">CUDA</span> <span class="n">install</span> <span class="n">root</span><span class="o">.</span>
<span class="n">Unable</span> <span class="n">to</span> <span class="n">load</span> <span class="n">extension</span> <span class="n">modelopt_cuda_ext</span> <span class="ow">and</span> <span class="n">falling</span> <span class="n">back</span> <span class="n">to</span> <span class="n">CPU</span> <span class="n">version</span><span class="o">.</span>
</pre></div>
</div>
<p>The installation of CUDA toolkit can be found in <a class="reference external" href="https://docs.nvidia.com/cuda/">CUDA Toolkit Documentation</a>.</p>
</li>
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