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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>
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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 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/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_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>
<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-next-on-trtllm.html">Deployment Guide for Qwen3 Next on TensorRT LLM - Blackwell &amp; Hopper Hardware</a></li>
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<li class="toctree-l1"><a class="reference internal" href="../models/adding-new-model.html">Adding a New Model</a></li>
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<p aria-level="2" class="caption" role="heading"><span class="caption-text">API Reference</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">Features</span></p>
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<li class="toctree-l1"><a class="reference internal" href="../features/feature-combination-matrix.html">Feature Combination Matrix</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/attention.html">Multi-Head, Multi-Query, and Group-Query Attention</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/disagg-serving.html">Disaggregated Serving</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/kvcache.html">KV Cache System</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/long-sequence.html">Long Sequences</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/lora.html">LoRA (Low-Rank Adaptation)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/multi-modality.html">Multimodal Support in TensorRT LLM</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/overlap-scheduler.html">Overlap Scheduler</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/paged-attention-ifb-scheduler.html">Paged Attention, IFB, and Request Scheduling</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/parallel-strategy.html">Parallelism in TensorRT LLM</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/quantization.html">Quantization</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/sampling.html">Sampling</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/additional-outputs.html">Additional Outputs</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/speculative-decoding.html">Speculative Decoding</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/checkpoint-loading.html">Checkpoint Loading</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/auto_deploy/auto-deploy.html">AutoDeploy (Prototype)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/ray-orchestrator.html">Ray Orchestrator (Prototype)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../features/torch_compile_and_piecewise_cuda_graph.html">Torch Compile &amp; Piecewise CUDA Graph</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="overview.html">Architecture Overview</a></li>
<li class="toctree-l1"><a class="reference internal" href="perf-analysis.html">Performance Analysis</a></li>
<li class="toctree-l1"><a class="reference internal" href="perf-benchmarking.html">TensorRT LLM Benchmarking</a></li>
<li class="toctree-l1"><a class="reference internal" href="ci-overview.html">Continuous Integration Overview</a></li>
<li class="toctree-l1"><a class="reference internal" href="dev-containers.html">Using Dev Containers</a></li>
<li class="toctree-l1"><a class="reference internal" href="api-change.html">LLM API Change Guide</a></li>
<li class="toctree-l1 current active"><a class="current reference internal" href="#">Introduction to KV Cache Transmission</a></li>
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<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/blog11_GPT_OSS_Eagle3.html">Running GPT-OSS-120B with Eagle3 Speculative Decoding on GB200/B200 (TensorRT LLM)</a></li>
<li class="toctree-l1"><a class="reference internal" href="../blogs/tech_blog/blog12_Combining_Guided_Decoding_and_Speculative_Decoding.html">Combining Guided Decoding and Speculative Decoding: Making CPU and GPU Cooperate Seamlessly</a></li>
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<li class="breadcrumb-item active" aria-current="page"><span class="ellipsis">Introduction to KV Cache Transmission</span></li>
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<section class="tex2jax_ignore mathjax_ignore" id="introduction-to-kv-cache-transmission">
<h1>Introduction to KV Cache Transmission<a class="headerlink" href="#introduction-to-kv-cache-transmission" title="Link to this heading">#</a></h1>
<p>This article provides a general overview of the components used for device-to-device transmission of KV cache, which is relied upon by dist-serving. It is intended as a reference for users who wish to understand the internal implementation or develop extended functionalities.</p>
<section id="table-of-contents">
<h2>Table of Contents<a class="headerlink" href="#table-of-contents" title="Link to this heading">#</a></h2>
<ul class="simple">
<li><p><a class="reference internal" href="#workflow">Workflow</a></p></li>
<li><p><a class="reference internal" href="#key-components">Key Components</a></p>
<ul>
<li><p><a class="reference internal" href="#transceiver">Transceiver</a></p></li>
<li><p><a class="reference internal" href="#sender-and-receiver">Sender and Receiver</a></p></li>
<li><p><a class="reference internal" href="#formatter">Formatter</a></p></li>
<li><p><a class="reference internal" href="#connection">Connection</a></p></li>
<li><p><a class="reference internal" href="#transfer-agent">Transfer Agent</a></p></li>
</ul>
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<li><p><a class="reference internal" href="#customization">Customization</a></p>
<ul>
<li><p><a class="reference internal" href="#encapsulation-and-overloading-of-low-level-communication-libraries">Encapsulation and Overloading of Low-Level Communication Libraries</a></p></li>
<li><p><a class="reference internal" href="#modifications-to-upper-level-runtime-logic">Modifications to Upper-Level Runtime Logic</a></p></li>
</ul>
</li>
<li><p><a class="reference internal" href="#evolution-outlook">Evolution Outlook</a></p></li>
</ul>
</section>
<section id="workflow">
<h2>Workflow<a class="headerlink" href="#workflow" title="Link to this heading">#</a></h2>
<img src="https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/media/kv_transfer.png?raw=true" alt="KV Cache Transfer Overview" width="500" height="auto">
<ol class="arabic simple">
<li><p>Context phase completes computation, KV cache stays in device memory awaiting transmission.</p></li>
<li><p>Context returns its communicator handle to the user, who selects the generation executor for continued communication.</p></li>
<li><p>If no prior connection exists, it’s established now. Generation phase shares its cache layout with context.</p></li>
<li><p>Generation phase requests KV cache for specific tokens.</p></li>
<li><p>Context sends KV cache to generation phase.</p></li>
<li><p>Generation phase resumes computation, context releases KV cache.</p></li>
</ol>
</section>
<section id="key-components">
<h2>Key Components<a class="headerlink" href="#key-components" title="Link to this heading">#</a></h2>
<section id="transceiver">
<h3>Transceiver<a class="headerlink" href="#transceiver" title="Link to this heading">#</a></h3>
<p>Responsible for coordinating the sending and receiving of cache among different ranks within the same executor.</p>
</section>
<section id="sender-and-receiver">
<h3>Sender and Receiver<a class="headerlink" href="#sender-and-receiver" title="Link to this heading">#</a></h3>
<p>Responsible for transmitting control plane messages. That is, during per-request transmission, the receiver bound to the generation informs the sender of the specific information it requires. The sender then sends the corresponding KV cache based on these messages.</p>
</section>
<section id="formatter">
<h3>Formatter<a class="headerlink" href="#formatter" title="Link to this heading">#</a></h3>
<p>Performs KV cache data transmission and correctly handles the mapping between caches across different TP/PP configurations.</p>
</section>
<section id="connection">
<h3>Connection<a class="headerlink" href="#connection" title="Link to this heading">#</a></h3>
<p>Bidirectional byte-stream protocol facility. Apart from essential operations such as connection establishment, it mainly provides send and receive functionalities. UCX accesses the system through this facility. The <code class="docutils literal notranslate"><span class="pre">AgentConnection</span></code> data structure adapts the upper-layer bidirectional send/receive semantics into a unidirectional read/write operation model.</p>
</section>
<section id="transfer-agent">
<h3>Transfer Agent<a class="headerlink" href="#transfer-agent" title="Link to this heading">#</a></h3>
<p>Unidirectional byte-stream read/write protocol facility. Apart from essential operations such as connection establishment, it primarily provides read and write functionalities. NIXL accesses the system through this facility.</p>
</section>
</section>
<section id="customization">
<h2>Customization<a class="headerlink" href="#customization" title="Link to this heading">#</a></h2>
<p>At the current stage, the customization work mainly involves inheriting the low-level data plane interfaces to enable the invocation of third-party communication libraries, as well as defining the data structures required for establishing connections in the data plane.</p>
<section id="encapsulation-and-overloading-of-low-level-communication-libraries">
<h3>Encapsulation and Overloading of Low-Level Communication Libraries<a class="headerlink" href="#encapsulation-and-overloading-of-low-level-communication-libraries" title="Link to this heading">#</a></h3>
<p>Each layer of interface described in the previous section supports overloading. Here, based on whether the underlying library uses a unidirectional or bidirectional protocol, we describe the customization methods respectively.</p>
<p>If the underlying library you are integrating uses a unidirectional communication model, with read/write as its primary interfaces, you should inherit the <code class="docutils literal notranslate"><span class="pre">executor::kv_cache::BaseTransferAgent</span></code> data structure. This structure mainly provides interfaces for memory registration, remote agent loading, and transfer request submission.</p>
<p>If the underlying library you are integrating uses a bidirectional communication model, you should inherit the <code class="docutils literal notranslate"><span class="pre">executor::kv_cache::Connection</span></code> data structure. This structure mainly provides send and receive interfaces.</p>
</section>
<section id="modifications-to-upper-level-runtime-logic">
<h3>Modifications to Upper-Level Runtime Logic<a class="headerlink" href="#modifications-to-upper-level-runtime-logic" title="Link to this heading">#</a></h3>
<p>This corresponds to the communication info section shown in the figure above. Since different underlying communication connections may require completely different setup methods—for example, some use IP and port, others require a world rank, and some communication libraries establish connections using binary-transparent metadata—we provide sufficient flexibility to allow users to customize this part as needed.</p>
</section>
</section>
<section id="evolution-outlook">
<h2>Evolution Outlook<a class="headerlink" href="#evolution-outlook" title="Link to this heading">#</a></h2>
<p>Currently, the architecture of KV transfer is being optimized. First, we plan to move the control plane logic up to Python to enable better integration with the Python runtime. In addition, we are reevaluating the current design choice of initiating communication only after the context computation is completed, which was originally made for flexibility. Lastly, since some control logic is still being transmitted through the data plane, we aim to clarify the relationship between the control and data planes, and to simplify and streamline the code logic of the data plane. Due to the modular architecture, these iterative enhancements are only loosely coupled with the <code class="docutils literal notranslate"><span class="pre">TransferAgent</span></code>. We aim to minimize the impact of future upgrades on third-party integrations.</p>
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