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| <link rel="modulepreload" href="/docs/inference-endpoints/pr_136/en/_app/immutable/chunks/getInferenceSnippets.1e3ae0bf.js"><!-- HEAD_svelte-u9bgzb_START --><meta name="hf:doc:metadata" content="{"title":"vLLM","local":"vllm","sections":[{"title":"Configuration","local":"configuration","sections":[],"depth":2},{"title":"Supported models","local":"supported-models","sections":[],"depth":2},{"title":"References","local":"references","sections":[],"depth":2}],"depth":1}"><!-- HEAD_svelte-u9bgzb_END --> <p></p> <h1 class="relative group"><a id="vllm" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#vllm"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>vLLM</span></h1> <p data-svelte-h="svelte-3mges0">vLLM is a high-performance, memory-efficient inference engine for open-source LLMs. It delivers efficient scheduling, KV-cache handling, | |
| batching, and decoding—all wrapped in a production-ready server. For most use cases, TGI, vLLM, and SGLang will be equivalently good options.</p> <p data-svelte-h="svelte-wxr29x"><strong>Core features</strong>:</p> <ul data-svelte-h="svelte-j2rm7r"><li><strong>PagedAttention for memory efficiency</strong></li> <li><strong>Continuous batching</strong></li> <li><strong>Optimized CUDA/HIP execution</strong></li> <li><strong>Speculative decoding & chunked prefill</strong></li> <li><strong>Multi-backend and hardware support</strong>: Runs across NVIDIA, AMD, and AWS Neuron to name a few</li></ul> <h2 class="relative group"><a id="configuration" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#configuration"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Configuration</span></h2> <p data-svelte-h="svelte-mut1up"><img src="https://raw.githubusercontent.com/huggingface/hf-endpoints-documentation/main/assets/vllm/vllm_config.png" alt="config"></p> <ul data-svelte-h="svelte-q07o07"><li><strong>Max Number of Sequences</strong>: The maximum number of sequences (requests) that can be processed together in a single batch. Controls | |
| the batch size by sequence count, affecting throughput and memory usage. For example, if max_num_seqs=8, up to 8 different prompts can | |
| be handled at once, regardless of their individual lengths, as long as the total token count also fits within the Max Number of Batched Tokens.</li> <li><strong>Max Number of Batched Tokens</strong>: The maximum total number of tokens (summed across all sequences) that can be processed in a single | |
| batch. Limits batch size by token count, balancing throughput and GPU memory allocation.</li> <li><strong>Tensor Parallel Size</strong>: The number of GPUs across which model weights are split within each layer. Increasing this allows larger | |
| models to run and frees up GPU memory for KV cache, but may introduce synchronization overhead.</li> <li><strong>KV Cache DType</strong>: the data type used for storing the key-value cache during generation. Options include “auto”, “fp8”, “fp8_e5m2”, | |
| and “fp8_e4m3”. Using lower precision types can reduce memory usage but may slightly impact generation quality.</li></ul> <p data-svelte-h="svelte-ylkbud">For more advanced configuration you can pass any of the <a href="https://docs.vllm.ai/en/stable/api/vllm/engine/arg_utils.html#vllm.engine.arg_utils.EngineArgs" rel="nofollow">Engine Arguments that vLLM supports</a> | |
| as container arguments. For example changing the <code>enable_lora</code> to <code>true</code> would look like this:</p> <p data-svelte-h="svelte-lhbgtj"><img src="https://raw.githubusercontent.com/huggingface/hf-endpoints-documentation/main/assets/vllm/vllm-advanced.png" alt="vllm-advanced"></p> <h2 class="relative group"><a id="supported-models" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#supported-models"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Supported models</span></h2> <p data-svelte-h="svelte-1ebg7ne">vLLM has wide support for large language models and embedding models. We recommend reading the | |
| <a href="https://docs.vllm.ai/en/stable/models/supported_models.html?h=supported+models" rel="nofollow">supported models</a> section in the vLLM documentation for a full list.</p> <p data-svelte-h="svelte-3apbod">vLLM also supports model implementations that are available in Transformers. Currently not all models work but support is planned for most | |
| decoder language models are supported, and vision language models.</p> <h2 class="relative group"><a id="references" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#references"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>References</span></h2> <p data-svelte-h="svelte-1lr4w3">We also recommend reading the <a href="https://docs.vllm.ai/en/stable/" rel="nofollow">vLLM documentation</a> for more in-depth information.</p> <a class="!text-gray-400 !no-underline text-sm flex items-center not-prose mt-4" href="https://github.com/huggingface/hf-endpoints-documentation/blob/main/docs/source/engines/vllm.mdx" target="_blank"><span data-svelte-h="svelte-1kd6by1"><</span> <span data-svelte-h="svelte-x0xyl0">></span> <span data-svelte-h="svelte-1dajgef"><span class="underline ml-1.5">Update</span> on GitHub</span></a> <p></p> | |
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