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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 has-children"><a class="reference internal" href="../installation/index.html">Installation</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="../installation/containers.html">Pre-built release container images on NGC</a></li>
<li class="toctree-l2"><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-l2"><a class="reference internal" href="../installation/build-from-source-linux.html">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>
<ul class="nav bd-sidenav">
<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/curl_responses_client.html">Curl Responses 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>
<li class="toctree-l2"><a class="reference internal" href="../examples/openai_responses_client.html">OpenAI Responses Client</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>
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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-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="current nav bd-sidenav">
<li class="toctree-l1"><a class="reference internal" href="feature-combination-matrix.html">Feature Combination Matrix</a></li>
<li class="toctree-l1"><a class="reference internal" href="attention.html">Multi-Head, Multi-Query, and Group-Query Attention</a></li>
<li class="toctree-l1"><a class="reference internal" href="disagg-serving.html">Disaggregated Serving</a></li>
<li class="toctree-l1"><a class="reference internal" href="kvcache.html">KV Cache System</a></li>
<li class="toctree-l1"><a class="reference internal" href="long-sequence.html">Long Sequences</a></li>
<li class="toctree-l1"><a class="reference internal" href="lora.html">LoRA (Low-Rank Adaptation)</a></li>
<li class="toctree-l1 current active"><a class="current reference internal" href="#">Multimodal Support in TensorRT LLM</a></li>
<li class="toctree-l1"><a class="reference internal" href="overlap-scheduler.html">Overlap Scheduler</a></li>
<li class="toctree-l1"><a class="reference internal" href="paged-attention-ifb-scheduler.html">Paged Attention, IFB, and Request Scheduling</a></li>
<li class="toctree-l1"><a class="reference internal" href="parallel-strategy.html">Parallelism in TensorRT LLM</a></li>
<li class="toctree-l1"><a class="reference internal" href="quantization.html">Quantization</a></li>
<li class="toctree-l1"><a class="reference internal" href="sampling.html">Sampling</a></li>
<li class="toctree-l1"><a class="reference internal" href="additional-outputs.html">Additional Outputs</a></li>
<li class="toctree-l1"><a class="reference internal" href="guided-decoding.html">Guided Decoding</a></li>
<li class="toctree-l1"><a class="reference internal" href="speculative-decoding.html">Speculative Decoding</a></li>
<li class="toctree-l1"><a class="reference internal" href="checkpoint-loading.html">Checkpoint Loading</a></li>
<li class="toctree-l1"><a class="reference internal" href="auto_deploy/auto-deploy.html">AutoDeploy (Prototype)</a></li>
<li class="toctree-l1"><a class="reference internal" href="ray-orchestrator.html">Ray Orchestrator (Prototype)</a></li>
<li class="toctree-l1"><a class="reference internal" href="torch_compile_and_piecewise_cuda_graph.html">Torch Compile &amp; Piecewise CUDA Graph</a></li>
<li class="toctree-l1"><a class="reference internal" href="helix.html">Helix Parallelism</a></li>
<li class="toctree-l1"><a class="reference internal" href="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>
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<li class="breadcrumb-item active" aria-current="page"><span class="ellipsis">Multimodal Support in TensorRT LLM</span></li>
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<section class="tex2jax_ignore mathjax_ignore" id="multimodal-support-in-tensorrt-llm">
<h1>Multimodal Support in TensorRT LLM<a class="headerlink" href="#multimodal-support-in-tensorrt-llm" title="Link to this heading">#</a></h1>
<p>TensorRT LLM supports a variety of multimodal models, enabling efficient inference with inputs beyond just text.</p>
<hr class="docutils" />
<section id="background">
<h2>Background<a class="headerlink" href="#background" title="Link to this heading">#</a></h2>
<p>Multimodal LLMs typically handle non-text inputs by combining a multimodal encoder with an LLM decoder. The encoder first transforms non-text modality input into embeddings, which are then fused with text embeddings and fed into the LLM decoder for downstream inference. Compared to standard LLM inference, multimodal LLM inference involves three additional stages to support non-text modalities.</p>
<ul class="simple">
<li><p><strong>Multimodal Input Processor</strong>: Preprocess raw multimodal input into a format suitable for the multimodal encoder, such as pixel values for vision models.</p></li>
<li><p><strong>Multimodal Encoder</strong>: Encodes the processed input into embeddings that are aligned with the LLM’s embedding space.</p></li>
<li><p><strong>Integration with LLM Decoder</strong>: Fuses multimodal embeddings with text embeddings as the input to the LLM decoder.</p></li>
</ul>
</section>
<section id="optimizations">
<h2>Optimizations<a class="headerlink" href="#optimizations" title="Link to this heading">#</a></h2>
<p>TensorRT LLM incorporates some key optimizations to enhance the performance of multimodal inference:</p>
<ul class="simple">
<li><p><strong>In-Flight Batching</strong>: Batches multimodal requests within the GPU executor to improve GPU utilization and throughput.</p></li>
<li><p><strong>CPU/GPU Concurrency</strong>: Asynchronously overlaps data preprocessing on the CPU with image encoding on the GPU.</p></li>
<li><p><strong>Raw data hashing</strong>: Leverages image hashes and token chunk information to improve KV cache reuse and minimize collisions.</p></li>
</ul>
<p>Further optimizations are under development and will be updated as they become available.</p>
</section>
<section id="model-support-matrix">
<h2>Model Support Matrix<a class="headerlink" href="#model-support-matrix" title="Link to this heading">#</a></h2>
<p>Please refer to the latest multimodal <a class="reference internal" href="../models/supported-models.html#multimodal-feature-support-matrix-pytorch-backend"><span class="std std-ref">support matrix</span></a>.</p>
</section>
<section id="examples">
<h2>Examples<a class="headerlink" href="#examples" title="Link to this heading">#</a></h2>
<p>The following examples demonstrate how to use TensorRT LLM’s multimodal support in various scenarios, including quick run examples, serving endpoints, and performance benchmarking.</p>
<section id="quick-start">
<h3>Quick start<a class="headerlink" href="#quick-start" title="Link to this heading">#</a></h3>
<p>Quickly try out TensorRT LLM’s multimodal support using our <code class="docutils literal notranslate"><span class="pre">LLM-API</span></code> and a ready-to-run <a class="reference external" href="https://github.com/NVIDIA/TensorRT-LLM/tree/9ba1426/examples/llm-api/quickstart_multimodal.py">example</a>:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>python3<span class="w"> </span>quickstart_multimodal.py<span class="w"> </span>--model_dir<span class="w"> </span>Efficient-Large-Model/NVILA-8B<span class="w"> </span>--modality<span class="w"> </span>image<span class="w"> </span>--disable_kv_cache_reuse
</pre></div>
</div>
</section>
<section id="openai-compatible-server-via-trtllm-serve">
<h3>OpenAI-Compatible Server via <a class="reference internal" href="../commands/trtllm-serve/trtllm-serve.html"><span class="std std-doc"><code class="docutils literal notranslate"><span class="pre">trtllm-serve</span></code></span></a><a class="headerlink" href="#openai-compatible-server-via-trtllm-serve" title="Link to this heading">#</a></h3>
<p>Launch an OpenAI-compatible server with multimodal support using the <code class="docutils literal notranslate"><span class="pre">trtllm-serve</span></code> command, for example:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>trtllm-serve<span class="w"> </span>Qwen/Qwen2-VL-7B-Instruct<span class="w"> </span>--backend<span class="w"> </span>pytorch
</pre></div>
</div>
<p>You can then send OpenAI-compatible requests, such as via curl or API clients, to the server endpoint. See <a class="reference external" href="https://github.com/NVIDIA/TensorRT-LLM/tree/9ba1426/examples/serve/curl_chat_client_for_multimodal.sh">curl chat client for multimodal script</a> as an example.</p>
</section>
<section id="run-with-trtllm-bench">
<h3>Run with <a class="reference internal" href="../commands/trtllm-bench.html"><span class="std std-doc"><code class="docutils literal notranslate"><span class="pre">trtllm-bench</span></code></span></a><a class="headerlink" href="#run-with-trtllm-bench" title="Link to this heading">#</a></h3>
<p>Evaluate offline inference performance with multimodal inputs using the <code class="docutils literal notranslate"><span class="pre">trtllm-bench</span></code> tool. For detailed instructions, see the <a class="reference internal" href="#../../source/performance/perf-benchmarking.md"><span class="xref myst">benchmarking guide</span></a>.</p>
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<p>This page is generated by TensorRT-LLM commit <a href="https://github.com/NVIDIA/TensorRT-LLM/tree/9ba1426">9ba1426</a>.</p>
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