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	<meta charset="utf-8" /><meta name="hf:doc:metadata" content="{"title":"Supported Hardware Providers","local":"supported-hardware-providers","sections":[{"title":"NVIDIA GPUs","local":"nvidia-gpus","sections":[],"depth":2},{"title":"AMD GPUs","local":"amd-gpus","sections":[],"depth":2},{"title":"AWS Accelerators (Inferentia/Trainium)","local":"aws-accelerators-inferentiatrainium","sections":[],"depth":2},{"title":"Google TPUs","local":"google-tpus","sections":[],"depth":2}],"depth":1}">
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	<link rel="modulepreload" href="/docs/hugs/pr_13/en/_app/immutable/chunks/EditOnGithub.d1c48e3d.js"><!-- HEAD_svelte-u9bgzb_START --><meta name="hf:doc:metadata" content="{"title":"Supported Hardware Providers","local":"supported-hardware-providers","sections":[{"title":"NVIDIA GPUs","local":"nvidia-gpus","sections":[],"depth":2},{"title":"AMD GPUs","local":"amd-gpus","sections":[],"depth":2},{"title":"AWS Accelerators (Inferentia/Trainium)","local":"aws-accelerators-inferentiatrainium","sections":[],"depth":2},{"title":"Google TPUs","local":"google-tpus","sections":[],"depth":2}],"depth":1}"><!-- HEAD_svelte-u9bgzb_END --> <p></p> <h1 class="relative group"><a id="supported-hardware-providers" 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-hardware-providers"><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 Hardware Providers</span></h1> <p data-svelte-h="svelte-o0d4dl">HUGS are optimized for a wide-variety of accelerators for ML inference, and support across different accelerator families and providers will continue to grow exponentially.</p> <h2 class="relative group"><a id="nvidia-gpus" 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="#nvidia-gpus"><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>NVIDIA GPUs</span></h2> <p data-svelte-h="svelte-1eptsmk">NVIDIA GPUs are widely used for machine learning and AI applications, offering high performance and specialized hardware for deep learning tasks. NVIDIA’s CUDA platform provides a robust ecosystem for GPU-accelerated computing.</p> <p data-svelte-h="svelte-r710j6">Supported device(s):</p> <ul data-svelte-h="svelte-1xy7gow"><li><strong>NVIDIA A10G</strong>: 24GB GDDR6 memory, 9216 CUDA cores, 288 Tensor cores, 72 RT cores</li> <li><strong>NVIDIA L4</strong>: 24GB GDDR6 memory, 7168 CUDA cores, 224 Tensor cores, 56 RT cores</li> <li><strong>NVIDIA L40S</strong>: 48GB GDDR6 memory, 18176 CUDA cores, 568 Tensor cores, 142 RT cores</li> <li><strong>NVIDIA A100</strong>: 40/80GB HBM2e memory, 6912 CUDA cores, 432 Tensor cores, 108 RT cores</li> <li><strong>NVIDIA H100</strong>: 80GB HBM3 memory, 14592 CUDA cores, 456 Tensor cores, 144 RT cores</li></ul> <h2 class="relative group"><a id="amd-gpus" 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="#amd-gpus"><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>AMD GPUs</span></h2> <p data-svelte-h="svelte-1id81yq">AMD GPUs provide strong competition in the AI and machine learning space, offering high-performance computing capabilities with their CDNA architecture. AMD’s ROCm (Radeon Open Compute) platform enables GPU-accelerated computing on Linux systems.</p> <p data-svelte-h="svelte-r710j6">Supported device(s):</p> <ul data-svelte-h="svelte-kswfo6"><li><strong>AMD Instinct MI300X</strong>: 192GB HBM3 memory, 304 Compute Units, 4864 AI Accelerators</li></ul> <h2 class="relative group"><a id="aws-accelerators-inferentiatrainium" 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="#aws-accelerators-inferentiatrainium"><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>AWS Accelerators (Inferentia/Trainium)</span></h2> <p data-svelte-h="svelte-1tni5e8">AWS Inferentia2 is a custom-built accelerator designed specifically for high-performance, cost-effective machine learning inference.</p> <p data-svelte-h="svelte-r710j6">Supported device(s):</p> <ul data-svelte-h="svelte-7jaagk"><li><strong>AWS Inferentia2</strong>: Available in Amazon EC2 Inf2 instances, offering up to 12 Inferentia2 chips per instance. AWS Inferentia2 accelerators are optimized for deploying large language models and other compute-intensive ML workloads, providing high throughput and low latency for inference tasks. More information at <a href="https://aws.amazon.com/ec2/instance-types/inf2" rel="nofollow">Amazon EC2 Inf2 Instances</a>.</li> <li><strong>AWS Trainium</strong>: <em>Coming soon!</em></li></ul> <h2 class="relative group"><a id="google-tpus" 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="#google-tpus"><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>Google TPUs</span></h2> <p data-svelte-h="svelte-b65gt3"><em>Coming soon</em></p> <a class="!text-gray-400 !no-underline text-sm flex items-center not-prose mt-4" href="https://github.com/huggingface/hugs-docs/blob/main/docs/source/hardware.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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