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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>
<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>
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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>
<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>
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<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>
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<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>
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<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>
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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>
<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"><a class="reference internal" href="multi-modality.html">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 current active"><a class="current reference internal" href="#">Helix Parallelism</a></li>
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<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>
<li class="toctree-l1"><a class="reference internal" href="../developer-guide/ci-overview.html">Continuous Integration Overview</a></li>
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<li class="breadcrumb-item active" aria-current="page"><span class="ellipsis">Helix Parallelism</span></li>
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<section class="tex2jax_ignore mathjax_ignore" id="helix-parallelism">
<h1>Helix Parallelism<a class="headerlink" href="#helix-parallelism" title="Link to this heading">#</a></h1>
<p>Helix is a context parallelism (CP) technique for the decode/generation phase of LLM inference. Unlike traditional attention-FFN disaggregation (AFD) techniques, which spatially separate attention and FFN blocks onto different GPUs, Helix temporally separates them by reconfiguring the same GPUs.</p>
<p>For all details, see the original paper:
<a class="reference external" href="https://arxiv.org/pdf/2507.07120">Helix Parallelism: Rethinking Sharding Strategies for
Interactive Multi-Million-Token LLM Decoding</a></p>
<section id="how-helix-works">
<h2>How Helix Works<a class="headerlink" href="#how-helix-works" title="Link to this heading">#</a></h2>
<p>In Helix parallelism:</p>
<ul class="simple">
<li><p><strong>KV cache distribution</strong>: The KV cache is partitioned across CP ranks during generation, with each rank responsible for a portion of the cached context</p></li>
<li><p><strong>Attention computation</strong>: Each rank computes partial attention over its local KV cache shard</p></li>
<li><p><strong>Attention postprocessing</strong>: Partial results are combined / corrected across ranks to produce the final attention output</p></li>
<li><p><strong>FFN layers</strong>: CP ranks are repurposed as tensor parallelism (TP) ranks for FFN/MoE layers, maximizing GPU utilization</p></li>
</ul>
</section>
<section id="when-to-use-helix">
<h2>When to Use Helix<a class="headerlink" href="#when-to-use-helix" title="Link to this heading">#</a></h2>
<p>Helix parallelism provides performance benefits when <strong>all</strong> of the following conditions apply:</p>
<ol class="arabic simple">
<li><p><strong>Disaggregated serving</strong>: Helix is designed for generation servers in a disaggregated (prefill/decode split) deployment architecture</p></li>
<li><p><strong>Long input sequences</strong>: Performance gains typically appear with input sequence lengths <strong>&gt;64K tokens</strong> or more</p></li>
<li><p><strong>Low batch sizes</strong>: Optimal for latency-sensitive workloads with high tokens/second/user requirements</p></li>
</ol>
<p>On a typical latency vs. throughput Pareto curve, Helix targets operating points toward the right side (low latency, high per-user throughput).</p>
</section>
<section id="supported-models">
<h2>Supported Models<a class="headerlink" href="#supported-models" title="Link to this heading">#</a></h2>
<p>Helix parallelism currently supports models using <strong>Multi-head Latent Attention (MLA)</strong> on Blackwell GPU architecture:</p>
<ul class="simple">
<li><p>DeepSeek-V3 / DeepSeek-V3-Lite</p></li>
</ul>
</section>
<section id="configuration">
<h2>Configuration<a class="headerlink" href="#configuration" title="Link to this heading">#</a></h2>
<section id="configuration-parameters">
<h3>Configuration Parameters<a class="headerlink" href="#configuration-parameters" title="Link to this heading">#</a></h3>
<p>Please set the following parameters for the generation servers in disaggregated mode. Example can be seen in the e2e accuracy test mentioned below.</p>
<div class="pst-scrollable-table-container"><table class="table">
<thead>
<tr class="row-odd"><th class="head"><p>Parameter</p></th>
<th class="head"><p>Description</p></th>
<th class="head"><p>Required</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">context_parallel_size</span></code></p></td>
<td><p>Number of GPUs for context parallelism (≥2 for Helix)</p></td>
<td><p>Yes</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">cp_config.cp_type</span></code></p></td>
<td><p>Must be <code class="docutils literal notranslate"><span class="pre">&quot;HELIX&quot;</span></code> or <code class="docutils literal notranslate"><span class="pre">CpType.HELIX</span></code></p></td>
<td><p>Yes</p></td>
</tr>
<tr class="row-even"><td><p><code class="docutils literal notranslate"><span class="pre">cp_config.tokens_per_block</span></code></p></td>
<td><p>Tokens per KV cache block</p></td>
<td><p>Yes</p></td>
</tr>
<tr class="row-odd"><td><p><code class="docutils literal notranslate"><span class="pre">kv_cache_config.tokens_per_block</span></code></p></td>
<td><p>Must match <code class="docutils literal notranslate"><span class="pre">cp_config.tokens_per_block</span></code></p></td>
<td><p>Yes</p></td>
</tr>
</tbody>
</table>
</div>
</section>
<section id="json-configuration-for-yaml-json-configs">
<h3>JSON Configuration (for YAML/JSON configs)<a class="headerlink" href="#json-configuration-for-yaml-json-configs" title="Link to this heading">#</a></h3>
<div class="highlight-json notranslate"><div class="highlight"><pre><span></span><span class="p">{</span>
<span class="w"> </span><span class="nt">&quot;context_parallel_size&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">2</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;cp_config&quot;</span><span class="p">:</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="nt">&quot;cp_type&quot;</span><span class="p">:</span><span class="w"> </span><span class="s2">&quot;HELIX&quot;</span><span class="p">,</span>
<span class="w"> </span><span class="nt">&quot;tokens_per_block&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">32</span>
<span class="w"> </span><span class="p">},</span>
<span class="w"> </span><span class="nt">&quot;kv_cache_config&quot;</span><span class="p">:</span><span class="w"> </span><span class="p">{</span>
<span class="w"> </span><span class="nt">&quot;tokens_per_block&quot;</span><span class="p">:</span><span class="w"> </span><span class="mi">32</span>
<span class="w"> </span><span class="p">}</span>
<span class="p">}</span>
</pre></div>
</div>
</section>
</section>
<section id="testing-helix-with-tensorrt-llm">
<h2>Testing Helix with TensorRT-LLM<a class="headerlink" href="#testing-helix-with-tensorrt-llm" title="Link to this heading">#</a></h2>
<section id="unit-test-mla-module-correctness">
<h3>Unit Test: MLA Module Correctness<a class="headerlink" href="#unit-test-mla-module-correctness" title="Link to this heading">#</a></h3>
<p>The simplest correctness test validates the <a class="reference download internal" download="" href="../_downloads/b509390ba70e52fabb10dbd9d15d5118/attention.py"><span class="xref download myst">MLA attention module</span></a> with Helix enabled:</p>
<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span><span class="c1"># Run the MLA Helix unit test</span>
pytest<span class="w"> </span>tests/unittest/_torch/modules/test_mla_helix.py<span class="w"> </span>-v
</pre></div>
</div>
<p>This test verifies that attention outputs match between single-GPU and Helix-parallelized execution.</p>
</section>
<section id="end-to-end-accuracy-test">
<h3>End-to-End Accuracy test<a class="headerlink" href="#end-to-end-accuracy-test" title="Link to this heading">#</a></h3>
<p>For end-to-end validation, the accuracy benchmark evaluates DeepSeek-V3-Lite in disaggregated mode on MMLU and GSM8K benchmarks:</p>
<p>Test location: <code class="docutils literal notranslate"><span class="pre">tests/integration/defs/accuracy/test_disaggregated_serving.py</span></code><br />
Test name: <code class="docutils literal notranslate"><span class="pre">TestDeepSeekV3Lite::test_auto_dtype_with_helix</span></code></p>
<p>This test demonstrates proper disaggregated server configuration with Helix.</p>
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