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Update gh-pages for pic hyperlinks. (#1973) 
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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="../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="../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="installing-on-linux">
<span id="linux"></span><h1>Installing on Linux<a class="headerlink" href="#installing-on-linux" title="Link to this heading"></a></h1>
<ol class="arabic">
<li><p>Retrieve and launch the docker container (optional).</p>
<p>You can pre-install the environment using the <a class="reference external" href="https://docs.nvidia.com/datacenter/cloud-native/container-toolkit">NVIDIA Container Toolkit</a> to avoid manual environment configuration.</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># Obtain and start the basic docker image environment (optional).</span>
docker<span class="w"> </span>run<span class="w"> </span>--rm<span class="w"> </span>--ipc<span class="o">=</span>host<span class="w"> </span>--runtime<span class="o">=</span>nvidia<span class="w"> </span>--gpus<span class="w"> </span>all<span class="w"> </span>--entrypoint<span class="w"> </span>/bin/bash<span class="w"> </span>-it<span class="w"> </span>nvidia/cuda:12.4.1-devel-ubuntu22.04
</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>
<li><p>Install TensorRT-LLM.</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># Install dependencies, TensorRT-LLM requires Python 3.10</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>python3.10<span class="w"> </span>python3-pip<span class="w"> </span>openmpi-bin<span class="w"> </span>libopenmpi-dev<span class="w"> </span>git<span class="w"> </span>git-lfs
<span class="c1"># Install the latest preview version (corresponding to the main branch) of TensorRT-LLM.</span>
<span class="c1"># If you want to install the stable version (corresponding to the release branch), please</span>
<span class="c1"># remove the `--pre` option.</span>
pip3<span class="w"> </span>install<span class="w"> </span>tensorrt_llm<span class="w"> </span>-U<span class="w"> </span>--pre<span class="w"> </span>--extra-index-url<span class="w"> </span>https://pypi.nvidia.com
<span class="c1"># Check installation</span>
python3<span class="w"> </span>-c<span class="w"> </span><span class="s2">&quot;import tensorrt_llm&quot;</span>
</pre></div>
</div>
<p>Please note that TensorRT-LLM depends on TensorRT. In earlier versions that include TensorRT 8,
overwriting an upgraded to a new version may require explicitly running <code class="docutils literal notranslate"><span class="pre">pip</span> <span class="pre">uninstall</span> <span class="pre">tensorrt</span></code>
to uninstall the old version.</p>
</li>
<li><p>Install the requirements for running the example.</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>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
pip<span class="w"> </span>install<span class="w"> </span>-r<span class="w"> </span>examples/bloom/requirements.txt
git<span class="w"> </span>lfs<span class="w"> </span>install
</pre></div>
</div>
</li>
</ol>
<p>Beyond the local execution, you can also use the NVIDIA Triton Inference Server to create a production-ready deployment of your LLM as described in this <a class="reference external" href="https://developer.nvidia.com/blog/optimizing-inference-on-llms-with-tensorrt-llm-now-publicly-available/">Optimizing Inference on Large Language Models with NVIDIA TensorRT-LLM</a> blog.</p>
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