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<p 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="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>
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<li class="toctree-l1"><a class="reference internal" href="advanced/gpt-attention.html">Multi-Head, Multi-Query, and Group-Query Attention</a></li>
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<li class="toctree-l1"><a class="reference internal" href="advanced/lora.html">Run gpt-2b + LoRA using GptManager / cpp runtime</a></li>
<li class="toctree-l1"><a class="reference internal" href="advanced/expert-parallelism.html">Expert Parallelism in TensorRT-LLM</a></li>
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<li class="toctree-l1"><a class="reference internal" href="advanced/disaggregated-service.html">Disaggregated-Service (experimental)</a></li>
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<li class="toctree-l1"><a class="reference internal" href="reference/memory.html">Memory Usage of TensorRT-LLM</a></li>
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<li class="toctree-l1"><a class="reference internal" href="blogs/H100vsA100.html">H100 has 4.6x A100 Performance in TensorRT-LLM, achieving 10,000 tok/s at 100ms to first token</a></li>
<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>
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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 setup and send HTTP requests using TensorRT-LLM.</p>
<section id="prerequisites">
<h2>Prerequisites<a class="headerlink" href="#prerequisites" title="Link to this heading"></a></h2>
<ul>
<li><p>This quick start uses the Meta Llama 3.1 model. This model is subject to a particular <a class="reference external" href="https://llama.meta.com/llama-downloads/">license</a>. To download the model files, agree to the terms and <a class="reference external" href="https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct?clone=true">authenticate with Hugging Face</a>.</p></li>
<li><p>Complete the <a class="reference internal" href="installation/linux.html"><span class="std std-doc">installation</span></a> steps.</p></li>
<li><p>Pull the weights and tokenizer files for the chat-tuned variant of the Llama 3.1 8B model from the <a class="reference external" href="https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct">Hugging Face Hub</a>.</p>
<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">git clone https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct</span>
</pre></div>
</div>
</li>
</ul>
</section>
<section id="llm-api">
<h2>LLM API<a class="headerlink" href="#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="n">prompts</span> <span class="o">=</span> <span class="p">[</span>
<span class="linenos"> 7</span> <span class="s2">&quot;Hello, my name is&quot;</span><span class="p">,</span>
<span class="linenos"> 8</span> <span class="s2">&quot;The president of the United States is&quot;</span><span class="p">,</span>
<span class="linenos"> 9</span> <span class="s2">&quot;The capital of France is&quot;</span><span class="p">,</span>
<span class="linenos">10</span> <span class="s2">&quot;The future of AI is&quot;</span><span class="p">,</span>
<span class="linenos">11</span> <span class="p">]</span>
<span class="linenos">12</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">13</span>
<span class="linenos">14</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">15</span>
<span class="linenos">16</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">17</span>
<span class="linenos">18</span> <span class="c1"># Print the outputs.</span>
<span class="linenos">19</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">20</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">21</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">22</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">23</span>
<span class="linenos">24</span>
<span class="linenos">25</span><span class="c1"># The entry point of the program need to be protected for spawning processes.</span>
<span class="linenos">26</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">27</span> <span class="n">main</span><span class="p">()</span>
</pre></div>
</div>
<p>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">API Introduction</span></a> and <a class="reference internal" href="llm-api-examples/index.html"><span class="doc std std-doc">LLM Examples Introduction</span></a>.</p>
</section>
<section id="compile-the-model-into-a-tensorrt-engine">
<span id="quick-start-guide-compile"></span><h2>Compile the Model into a TensorRT Engine<a class="headerlink" href="#compile-the-model-into-a-tensorrt-engine" title="Link to this heading"></a></h2>
<p>Use the <a class="reference external" href="https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/llama">Llama model definition</a> from the <code class="docutils literal notranslate"><span class="pre">examples/llama</span></code> directory of the GitHub repository.
The model definition is a minimal example that shows some of the optimizations available in TensorRT-LLM.</p>
<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="gp"># </span>From<span class="w"> </span>the<span class="w"> </span>root<span class="w"> </span>of<span class="w"> </span>the<span class="w"> </span>cloned<span class="w"> </span>repository,<span class="w"> </span>start<span class="w"> </span>the<span class="w"> </span>TensorRT-LLM<span class="w"> </span>container
<span class="go">make -C docker release_run LOCAL_USER=1</span>
<span class="gp"># </span>Log<span class="w"> </span><span class="k">in</span><span class="w"> </span>to<span class="w"> </span>huggingface-cli
<span class="gp"># </span>You<span class="w"> </span>can<span class="w"> </span>get<span class="w"> </span>your<span class="w"> </span>token<span class="w"> </span>from<span class="w"> </span>huggingface.co/settings/token
<span class="go">huggingface-cli login --token *****</span>
<span class="gp"># </span>Convert<span class="w"> </span>the<span class="w"> </span>model<span class="w"> </span>into<span class="w"> </span>TensorRT-LLM<span class="w"> </span>checkpoint<span class="w"> </span>format
<span class="go">cd examples/llama</span>
<span class="go">pip install -r requirements.txt</span>
<span class="go">pip install --upgrade transformers # Llama 3.1 requires transformer 4.43.0+ version.</span>
<span class="go">python3 convert_checkpoint.py --model_dir Meta-Llama-3.1-8B-Instruct --output_dir llama-3.1-8b-ckpt</span>
<span class="gp"># </span>Compile<span class="w"> </span>model
<span class="go">trtllm-build --checkpoint_dir llama-3.1-8b-ckpt \</span>
<span class="go"> --gemm_plugin float16 \</span>
<span class="go"> --output_dir ./llama-3.1-8b-engine</span>
</pre></div>
</div>
<p>When you create a model definition with the TensorRT-LLM API, you build a graph of operations from <a class="reference external" href="https://developer.nvidia.com/tensorrt">NVIDIA TensorRT</a> primitives that form the layers of your neural network. These operations map to specific kernels; prewritten programs for the GPU.</p>
<p>In this example, we included the <code class="docutils literal notranslate"><span class="pre">gpt_attention</span></code> plugin, which implements a FlashAttention-like fused attention kernel, and the <code class="docutils literal notranslate"><span class="pre">gemm</span></code> plugin, that performs matrix multiplication with FP32 accumulation. We also called out the desired precision for the full model as FP16, matching the default precision of the weights that you downloaded from Hugging Face. For more information about plugins and quantizations, refer to the <a class="reference external" href="https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/llama">Llama example</a> and <a class="reference internal" href="reference/precision.html#precision"><span class="std std-ref">Numerical Precision</span></a> section.</p>
</section>
<section id="run-the-model">
<h2>Run the Model<a class="headerlink" href="#run-the-model" title="Link to this heading"></a></h2>
<p>Now that you have the model engine, run the engine and perform inference.</p>
<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">python3 ../run.py --engine_dir ./llama-3.1-8b-engine --max_output_len 100 --tokenizer_dir Meta-Llama-3.1-8B-Instruct --input_text &quot;How do I count to nine in French?&quot;</span>
</pre></div>
</div>
</section>
<section id="deploy-with-triton-inference-server">
<h2>Deploy with Triton Inference Server<a class="headerlink" href="#deploy-with-triton-inference-server" title="Link to this heading"></a></h2>
<p>To create a production-ready deployment of your LLM, use the <a class="reference external" href="https://github.com/triton-inference-server/tensorrtllm_backend">Triton Inference Server backend for TensorRT-LLM</a> to leverage the TensorRT-LLM C++ runtime for rapid inference execution and include optimizations like in-flight batching and paged KV caching. Triton Inference Server with the TensorRT-LLM backend is available as a <a class="reference external" href="https://catalog.ngc.nvidia.com/orgs/nvidia/containers/tritonserver/tags">pre-built container through NVIDIA NGC</a>.</p>
<ol class="arabic simple">
<li><p>Clone the TensorRT-LLM backend repository:</p></li>
</ol>
<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="go">cd ..</span>
<span class="go">git clone https://github.com/triton-inference-server/tensorrtllm_backend.git</span>
<span class="go">cd tensorrtllm_backend</span>
</pre></div>
</div>
<ol class="arabic simple" start="2">
<li><p>Refer to <a class="reference external" href="https://github.com/triton-inference-server/tensorrtllm_backend/blob/main/docs/llama.md">End to end workflow to run llama 7b</a> in the TensorRT-LLM backend repository to deploy the model with Triton Inference Server.</p></li>
</ol>
</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>Installed and built TensorRT-LLM</p></li>
<li><p>Retrieved the model weights</p></li>
<li><p>Compiled and ran the model</p></li>
<li><p>Deployed the model with Triton Inference Server</p></li>
<li><p>As an alternative to deploying the engine with FastAPI-based OpenAI API Server, you can use the <a class="reference external" href="https://nvidia.github.io/TensorRT-LLM/commands/trtllm-serve.html"><code class="docutils literal notranslate"><span class="pre">trtllm-serve</span></code></a> CLI.</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://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/performance/perf-best-practices.md">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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