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TensorRT-LLMs/1.1.0rc2/quick-start-guide.html
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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 current active"><a class="current reference internal" href="#">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="installation/containers.html">Pre-built release container images on NGC</a></li>
<li class="toctree-l1"><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-l1"><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"><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-l1"><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-l1"><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-l1"><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>
<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_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>
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<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>
</ul>
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Model Definition API</span></p>
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<li class="toctree-l1"><a class="reference internal" href="python-api/tensorrt_llm.runtime.html">Runtime</a></li>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">Command-Line 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-build.html">trtllm-build</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">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>
<li class="toctree-l1"><a class="reference internal" href="architecture/workflow.html">TensorRT-LLM Build Workflow</a></li>
<li class="toctree-l1"><a class="reference internal" href="architecture/add-model.html">Adding a Model</a></li>
</ul>
<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 (Prototype)</a></li>
</ul>
<p aria-level="2" class="caption" role="heading"><span class="caption-text">Performance</span></p>
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<li class="toctree-l1"><a class="reference internal" href="performance/perf-benchmarking.html">Benchmarking</a></li>
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<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>
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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/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/blog10_ADP_Balance_Strategy.html">ADP Balance Strategy</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>
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<section id="quick-start-guide">
<span id="id1"></span><h1>Quick Start Guide<a class="headerlink" href="#quick-start-guide" title="Link to this heading">#</a></h1>
<p>This is the starting point to try out TensorRT-LLM. Specifically, this Quick Start Guide enables you to quickly get set up and send HTTP requests using TensorRT-LLM.</p>
<section id="installation">
<h2>Installation<a class="headerlink" href="#installation" title="Link to this heading">#</a></h2>
<p>There are multiple ways to install and run TensorRT-LLM. For most users, the options below should be ordered from simple to complex. The approaches are equivalent in terms of the supported features.</p>
<p>Note: <strong>This project will download and install additional third-party open source software projects. Review the license terms of these open source projects before use.</strong></p>
<ol class="arabic simple">
<li><p><a class="reference internal" href="installation/containers.html"><span class="doc std std-doc">Pre-built release container images on NGC</span></a></p></li>
<li><p>Pre-built release wheels on <a class="reference external" href="https://pypi.org/project/tensorrt-llm">PyPI</a> (see <a class="reference internal" href="installation/linux.html"><span class="doc std std-doc">Installing on Linux via pip</span></a>)</p></li>
<li><p><a class="reference internal" href="installation/build-from-source-linux.html"><span class="doc std std-doc">Building from source</span></a></p></li>
</ol>
<p>The following examples can most easily be executed using the prebuilt <a class="reference external" href="https://registry.ngc.nvidia.com/orgs/nvstaging/teams/tensorrt-llm/containers/release">Docker release container available on NGC</a> (see also <a class="reference external" href="https://github.com/NVIDIA/TensorRT-LLM/blob/main/docker/release.md">release.md</a> on GitHub). Ensure to run these commands as a user with appropriate permissions, preferably <code class="docutils literal notranslate"><span class="pre">root</span></code>, to streamline the setup process.</p>
</section>
<section id="launch-docker-on-a-node-with-nvidia-gpus-deployed">
<h2>Launch Docker on a node with NVIDIA GPUs deployed.<a class="headerlink" href="#launch-docker-on-a-node-with-nvidia-gpus-deployed" title="Link to this heading">#</a></h2>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>docker<span class="w"> </span>run<span class="w"> </span>--ipc<span class="w"> </span>host<span class="w"> </span>--gpus<span class="w"> </span>all<span class="w"> </span>-it<span class="w"> </span>nvcr.io/nvidia/tensorrt-llm/release
</pre></div>
</div>
</section>
<section id="run-offline-inference-with-llm-api">
<h2>Run Offline inference with LLM API<a class="headerlink" href="#run-offline-inference-with-llm-api" title="Link to this heading">#</a></h2>
<p>The LLM API is a Python API designed to facilitate setup and inference with TensorRT-LLM directly within Python. It enables model optimization by simply specifying a HuggingFace repository name or a model checkpoint. The LLM API streamlines the process by managing checkpoint conversion, engine building, engine loading, and model inference, all through a single Python object.</p>
<p>Here is a simple example to show how to use the LLM API with TinyLlama.</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">LLM</span><span class="p">,</span> <span class="n">SamplingParams</span>
<span class="linenos"> 2</span>
<span class="linenos"> 3</span>
<span class="linenos"> 4</span><span class="k">def</span><span class="w"> </span><span class="nf">main</span><span class="p">():</span>
<span class="linenos"> 5</span>
<span class="linenos"> 6</span> <span class="c1"># Model could accept HF model name, a path to local HF model,</span>
<span class="linenos"> 7</span> <span class="c1"># or TensorRT Model Optimizer&#39;s quantized checkpoints like nvidia/Llama-3.1-8B-Instruct-FP8 on HF.</span>
<span class="linenos"> 8</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"> 9</span>
<span class="linenos">10</span> <span class="c1"># Sample prompts.</span>
<span class="linenos">11</span> <span class="n">prompts</span> <span class="o">=</span> <span class="p">[</span>
<span class="linenos">12</span> <span class="s2">&quot;Hello, my name is&quot;</span><span class="p">,</span>
<span class="linenos">13</span> <span class="s2">&quot;The capital of France is&quot;</span><span class="p">,</span>
<span class="linenos">14</span> <span class="s2">&quot;The future of AI is&quot;</span><span class="p">,</span>
<span class="linenos">15</span> <span class="p">]</span>
<span class="linenos">16</span>
<span class="linenos">17</span> <span class="c1"># Create a sampling params.</span>
<span class="linenos">18</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">19</span>
<span class="linenos">20</span> <span class="k">for</span> <span class="n">output</span> <span class="ow">in</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">21</span> <span class="nb">print</span><span class="p">(</span>
<span class="linenos">22</span> <span class="sa">f</span><span class="s2">&quot;Prompt: </span><span class="si">{</span><span class="n">output</span><span class="o">.</span><span class="n">prompt</span><span class="si">!r}</span><span class="s2">, Generated text: </span><span class="si">{</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="si">!r}</span><span class="s2">&quot;</span>
<span class="linenos">23</span> <span class="p">)</span>
<span class="linenos">24</span>
<span class="linenos">25</span> <span class="c1"># Got output like</span>
<span class="linenos">26</span> <span class="c1"># Prompt: &#39;Hello, my name is&#39;, Generated text: &#39;\n\nJane Smith. I am a student pursuing my degree in Computer Science at [university]. I enjoy learning new things, especially technology and programming&#39;</span>
<span class="linenos">27</span> <span class="c1"># Prompt: &#39;The president of the United States is&#39;, Generated text: &#39;likely to nominate a new Supreme Court justice to fill the seat vacated by the death of Antonin Scalia. The Senate should vote to confirm the&#39;</span>
<span class="linenos">28</span> <span class="c1"># Prompt: &#39;The capital of France is&#39;, Generated text: &#39;Paris.&#39;</span>
<span class="linenos">29</span> <span class="c1"># Prompt: &#39;The future of AI is&#39;, Generated text: &#39;an exciting time for us. We are constantly researching, developing, and improving our platform to create the most advanced and efficient model available. We are&#39;</span>
<span class="linenos">30</span>
<span class="linenos">31</span>
<span class="linenos">32</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">33</span> <span class="n">main</span><span class="p">()</span>
</pre></div>
</div>
<p>You can also directly load TensorRT Model Optimizer’s <a class="reference external" href="https://huggingface.co/collections/nvidia/model-optimizer-66aa84f7966b3150262481a4">quantized checkpoints on Hugging Face</a> in the LLM constructor.
To learn more about the LLM API, check out the <a class="reference internal" href="llm-api/index.html"><span class="doc std std-doc">LLM API Introduction</span></a> and <a class="reference internal" href="examples/llm_api_examples.html"><span class="doc std std-doc">LLM Examples</span></a>.</p>
</section>
<section id="deploy-online-serving-with-trtllm-serve">
<span id="deploy-with-trtllm-serve"></span><h2>Deploy online serving with trtllm-serve<a class="headerlink" href="#deploy-online-serving-with-trtllm-serve" title="Link to this heading">#</a></h2>
<p>You can use the <code class="docutils literal notranslate"><span class="pre">trtllm-serve</span></code> command to start an OpenAI compatible server to interact with a model.
To start the server, you can run a command like the following example inside a Docker container:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>trtllm-serve<span class="w"> </span><span class="s2">&quot;TinyLlama/TinyLlama-1.1B-Chat-v1.0&quot;</span>
</pre></div>
</div>
<blockquote>
<div><p>[!NOTE]
If you are running <code class="docutils literal notranslate"><span class="pre">trtllm-server</span></code> inside a Docker container, you have two options for sending API requests:</p>
</div></blockquote>
<blockquote>
<div><ol class="arabic simple">
<li><p>Expose port <code class="docutils literal notranslate"><span class="pre">8000</span></code> to access the server from outside the container.</p></li>
</ol>
</div></blockquote>
<blockquote>
<div><ol class="arabic simple" start="2">
<li><p>Open a new terminal and use the following command to directly attach to the running container:</p></li>
</ol>
</div></blockquote>
<blockquote>
<div><div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>docker<span class="w"> </span><span class="nb">exec</span><span class="w"> </span>-it<span class="w"> </span>&lt;container_id&gt;<span class="w"> </span>bash
</pre></div>
</div>
</div></blockquote>
<p>After the server has started, you can access well-known OpenAI endpoints such as <code class="docutils literal notranslate"><span class="pre">v1/chat/completions</span></code>.
Inference can then be performed using examples similar to the one provided below, from a separate terminal.</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>curl<span class="w"> </span>-X<span class="w"> </span>POST<span class="w"> </span>http://localhost:8000/v1/chat/completions<span class="w"> </span><span class="se">\</span>
<span class="w"> </span>-H<span class="w"> </span><span class="s2">&quot;Content-Type: application/json&quot;</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>-H<span class="w"> </span><span class="s2">&quot;Accept: application/json&quot;</span><span class="w"> </span><span class="se">\</span>
<span class="w"> </span>-d<span class="w"> </span><span class="s1">&#39;{</span>
<span class="s1"> &quot;model&quot;: &quot;TinyLlama/TinyLlama-1.1B-Chat-v1.0&quot;,</span>
<span class="s1"> &quot;messages&quot;:[{&quot;role&quot;: &quot;system&quot;, &quot;content&quot;: &quot;You are a helpful assistant.&quot;},</span>
<span class="s1"> {&quot;role&quot;: &quot;user&quot;, &quot;content&quot;: &quot;Where is New York? Tell me in a single sentence.&quot;}],</span>
<span class="s1"> &quot;max_tokens&quot;: 32,</span>
<span class="s1"> &quot;temperature&quot;: 0</span>
<span class="s1"> }&#39;</span>
</pre></div>
</div>
<p><em>Example Output</em></p>
<div class="highlight-json notranslate"><div class="highlight"><pre><span></span><span class="p">{</span>
<span class="w"> </span><span class="nt">&quot;id&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;chatcmpl-ef648e7489c040679d87ed12db5d3214&quot;</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;object&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;chat.completion&quot;</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;created&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">1741966075</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;model&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;TinyLlama/TinyLlama-1.1B-Chat-v1.0&quot;</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;choices&quot;</span><span class="p">:</span><span class="w"> </span><span class="p">[</span>
<span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="nt">&quot;index&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">0</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;message&quot;</span><span class="p">:</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="nt">&quot;role&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;assistant&quot;</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;content&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;New York is a city in the northeastern United States, located on the eastern coast of the state of New York.&quot;</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;tool_calls&quot;</span><span class="p">:</span><span class="w"> </span><span class="p">[]</span>
<span class="w"> </span><span class="p">},</span>
<span class="w"> </span><span class="nt">&quot;logprobs&quot;</span><span class="p">:</span><span class="w"> </span><span class="kc">null</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;finish_reason&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;stop&quot;</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;stop_reason&quot;</span><span class="p">:</span><span class="w"> </span><span class="kc">null</span>
<span class="w"> </span><span class="p">}</span>
<span class="w"> </span><span class="p">],</span>
<span class="w"> </span><span class="nt">&quot;usage&quot;</span><span class="p">:</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="nt">&quot;prompt_tokens&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">43</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;total_tokens&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">69</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;completion_tokens&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">26</span>
<span class="w"> </span><span class="p">}</span>
<span class="p">}</span>
</pre></div>
</div>
<p>For detailed examples and command syntax, refer to the <a class="reference internal" href="#commands/trtllm-serve.rst"><span class="xref myst">trtllm-serve</span></a> section.</p>
<ol class="arabic simple">
<li><p>Expose port <code class="docutils literal notranslate"><span class="pre">8000</span></code> to access the server from outside the container.</p></li>
<li><p>Open a new terminal and use the following command to directly attach to the running container:</p></li>
</ol>
<div class="highlight-bash:docs/source/quick-start-guide.md notranslate"><div class="highlight"><pre><span></span>docker exec -it &lt;container_id&gt; bash
</pre></div>
</div>
</section>
<section id="next-steps">
<h2>Next Steps<a class="headerlink" href="#next-steps" title="Link to this heading">#</a></h2>
<p>In this Quick Start Guide, you:</p>
<ul class="simple">
<li><p>Saw an example of the LLM API</p></li>
<li><p>Learned about deploying a model with <code class="docutils literal notranslate"><span class="pre">trtllm-serve</span></code></p></li>
</ul>
<p>For more examples, refer to:</p>
<ul class="simple">
<li><p><a class="reference external" href="https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples">examples</a> for showcases of how to run a quick benchmark on latest LLMs.</p></li>
</ul>
</section>
<section id="related-information">
<h2>Related Information<a class="headerlink" href="#related-information" title="Link to this heading">#</a></h2>
<ul class="simple">
<li><p><a class="reference external" href="https://nvidia.github.io/TensorRT-LLM/performance/performance-tuning-guide/index.html">Best Practices Guide</a></p></li>
<li><p><a class="reference external" href="https://nvidia.github.io/TensorRT-LLM/reference/support-matrix.html">Support Matrix</a></p></li>
</ul>
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