{"id":2039,"date":"2025-12-27T20:51:43","date_gmt":"2025-12-27T20:51:43","guid":{"rendered":"https:\/\/ai-box.eu\/?p=2039"},"modified":"2025-12-27T21:31:06","modified_gmt":"2025-12-27T21:31:06","slug":"install-vllm-on-gigabyte-ai-top-atom-high-performance-llm-inference-with-openai-compatible-api-part-2-3","status":"publish","type":"post","link":"https:\/\/ai-box.eu\/en\/large-language-models-en\/install-vllm-on-gigabyte-ai-top-atom-high-performance-llm-inference-with-openai-compatible-api-part-2-3\/2039\/","title":{"rendered":"Install vLLM on Gigabyte AI TOP ATOM: High-Performance LLM Inference with OpenAI-Compatible API &#8211; Part 2-3"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_82_2 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/ai-box.eu\/en\/large-language-models-en\/install-vllm-on-gigabyte-ai-top-atom-high-performance-llm-inference-with-openai-compatible-api-part-2-3\/2039\/#Configuring_vLLM_for_Production_Deployment_More_Complex\" >Configuring vLLM for Production Deployment (More Complex)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/ai-box.eu\/en\/large-language-models-en\/install-vllm-on-gigabyte-ai-top-atom-high-performance-llm-inference-with-openai-compatible-api-part-2-3\/2039\/#Docker_Parameters_Used\" >Docker Parameters Used<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/ai-box.eu\/en\/large-language-models-en\/install-vllm-on-gigabyte-ai-top-atom-high-performance-llm-inference-with-openai-compatible-api-part-2-3\/2039\/#1_Runtime_Parameters\" >1. Runtime Parameters<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/ai-box.eu\/en\/large-language-models-en\/install-vllm-on-gigabyte-ai-top-atom-high-performance-llm-inference-with-openai-compatible-api-part-2-3\/2039\/#2_System_Network_Configuration\" >2. System &amp; Network Configuration<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/ai-box.eu\/en\/large-language-models-en\/install-vllm-on-gigabyte-ai-top-atom-high-performance-llm-inference-with-openai-compatible-api-part-2-3\/2039\/#3_The_Container_Image\" >3. The Container Image<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/ai-box.eu\/en\/large-language-models-en\/install-vllm-on-gigabyte-ai-top-atom-high-performance-llm-inference-with-openai-compatible-api-part-2-3\/2039\/#4_Execution_Logic_Bash_Scripting\" >4. Execution Logic (Bash Scripting)<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/ai-box.eu\/en\/large-language-models-en\/install-vllm-on-gigabyte-ai-top-atom-high-performance-llm-inference-with-openai-compatible-api-part-2-3\/2039\/#5_The_Python_Download_Script\" >5. The Python Download Script<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/ai-box.eu\/en\/large-language-models-en\/install-vllm-on-gigabyte-ai-top-atom-high-performance-llm-inference-with-openai-compatible-api-part-2-3\/2039\/#Firewall_Settings_Optional\" >Firewall Settings (Optional)<\/a><\/li><\/ul><\/li><\/ul><\/nav><\/div>\n<h2 data-path-to-node=\"24\"><span class=\"ez-toc-section\" id=\"Configuring_vLLM_for_Production_Deployment_More_Complex\"><\/span>Configuring vLLM for Production Deployment (More Complex)<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p data-path-to-node=\"25\">For production use, I want to run the container in the background and ensure that it starts automatically after a reboot. Additionally, I mount the Hugging Face cache so that models do not have to be redownloaded every time the container starts. This saves you the time otherwise required for the download and, in my opinion, leads to a more stable system. For one, the model as such cannot change, so you always have exactly the same behavior, and above all, you avoid potential problems with the download process itself and the many GBs that such a model encompasses.<\/p>\n<p data-path-to-node=\"25\">First, I create a directory on my server on a drive where the models should be stored. Many hundreds of GBs can accumulate here quickly. Therefore, think about how you best want to manage the models. Of course, you can later copy this folder along with the models to another drive. For now, I&#8217;m just creating the folder in my user&#8217;s home directory <code>~\/<\/code>.<\/p>\n<p data-path-to-node=\"25\"><strong>Command:<\/strong> <code>mkdir -p ~\/models<\/code><\/p>\n<p data-path-to-node=\"25\">Now I stop the test container (if it is still running) with <code>Ctrl+C<\/code>:<\/p>\n<p data-path-to-node=\"25\">In a next step, I restart the container in the background with a name, a restart policy, and the mounted model cache.<\/p>\n<p data-path-to-node=\"25\"><strong>Note:<\/strong> I needed several attempts here before I could successfully download the model at all. The reason for my problems was likely the Christmas season and the resulting high data volume with my internet service provider, presumably due to streaming &amp; co. In the <code>docker run<\/code> command that follows, all models are stored in your home folder in the subfolder <code>models<\/code>, each with the model name that you must change in the following command when you specify the model. I have highlighted the 3 points to be adjusted in bold in the command.<\/p>\n<p data-path-to-node=\"25\"><strong>Model:<\/strong> <code>Qwen2.5-Math-1.5B-Instruct<\/code><\/p>\n<p data-path-to-node=\"25\"><strong>Command:<\/strong> <code>docker run -it --rm --name <strong>vllm-Qwen2.5-Math-1.5B-Instruct<\/strong> -v ~\/models:\/data --dns 8.8.8.8 --dns 8.8.4.4 --ipc=host nvcr.io\/nvidia\/vllm:25.11-py3 \/bin\/bash -c \"pip install hf_transfer &amp;&amp; export HF_HUB_ENABLE_HF_TRANSFER=1 &amp;&amp; python3 -c \\\"from huggingface_hub import snapshot_download; snapshot_download(repo_id='<strong>Qwen\/Qwen2.5-Math-1.5B-Instruct<\/strong>', local_dir='\/data\/<strong>Qwen2.5-Math-1.5B-Instruct<\/strong>', max_workers=1, resume_download=True)\\\"\"<\/code><\/p>\n<p data-path-to-node=\"25\">Here is another model that I was able to download successfully.<\/p>\n<p data-path-to-node=\"25\"><strong>Model:<\/strong> <code>openai\/gpt-oss-20b<\/code><\/p>\n<p data-path-to-node=\"25\"><strong>Command:<\/strong> <code>docker run -it --rm --name <strong>vllm-gpt-oss-20b<\/strong> -v ~\/models:\/data --dns 8.8.8.8 --dns 8.8.4.4 --ipc=host nvcr.io\/nvidia\/vllm:25.11-py3 \/bin\/bash -c \"pip install hf_transfer &amp;&amp; export HF_HUB_ENABLE_HF_TRANSFER=1 &amp;&amp; python3 -c \\\"from huggingface_hub import snapshot_download; snapshot_download(repo_id='openai\/<strong>gpt-oss-20b<\/strong>', local_dir='\/data\/<strong>gpt-oss-20b<\/strong>', max_workers=1, resume_download=True)\\\"\"<\/code><\/p>\n<p data-path-to-node=\"25\">After the model has been successfully downloaded, I now start the vLLM server with the locally stored model. Important: I use the local path <code>\/data\/gpt-oss-20b<\/code> instead of the Hugging Face name:<\/p>\n<p data-path-to-node=\"25\"><strong>Command:<\/strong> <code>docker run -d --gpus all --name vllm-server -p 8000:8000 --restart unless-stopped -v ~\/models:\/data --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 nvcr.io\/nvidia\/vllm:25.11-py3 vllm serve \/data\/gpt-oss-20b --gpu-memory-utilization 0.6<\/code><\/p>\n<p data-path-to-node=\"25\">The parameter <code>-d<\/code> starts the container in the background, <code>--name vllm-server<\/code> gives it a name, <code>--restart unless-stopped<\/code> ensures that the container automatically restarts after a reboot, and <code>-v ~\/models:\/data<\/code> mounts the local model directory. With <code>\/data\/gpt-oss-20b<\/code>, I refer to the locally stored model.<\/p>\n<p data-path-to-node=\"25\">Now you can access the vLLM server from any computer in the network. Open <code>http:\/\/&lt;IP-Address-AI-TOP-ATOM&gt;:8000<\/code> in your browser or with cURL (replace <code>&lt;IP-Address-AI-TOP-ATOM&gt;<\/code> with the IP address of your AI TOP ATOM).<\/p>\n<p data-path-to-node=\"3\">This command is optimized to download huge model files from Hugging Face stably and performantly, bypassing typical sources of error (such as DNS problems or slow downloads). When I wrote this guide during the Christmas holidays 2025, my internet provider probably had massive bandwidth problems and I had to restart the download very frequently because even the provider&#8217;s DNS resolution no longer worked.<\/p>\n<h2 data-path-to-node=\"4\"><span class=\"ez-toc-section\" id=\"Docker_Parameters_Used\"><\/span>Docker Parameters Used<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>In the docker run command I used, I utilized a variety of flags that I would like to explain to you below. Whether this is truly useful for you, I cannot say, but when I wrote this report, I had problems with the internet connection and had to create a stable call with which I could download the approx. 65 GB of the gpt-oss-120b model. For testing purposes to make it faster, I used gpt-oss-20b.<\/p>\n<h3 data-path-to-node=\"4\"><span class=\"ez-toc-section\" id=\"1_Runtime_Parameters\"><\/span>1. Runtime Parameters<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>The following parameters all refer to how the created Docker container behaves at runtime.<\/p>\n<ul data-path-to-node=\"6\">\n<li>\n<p data-path-to-node=\"6,0,0\"><b data-path-to-node=\"6,0,0\" data-index-in-node=\"0\"><code data-path-to-node=\"6,0,0\" data-index-in-node=\"0\">docker run<\/code><\/b>: The base command for creating and starting a new container.<\/p>\n<\/li>\n<li>\n<p data-path-to-node=\"6,1,0\"><b data-path-to-node=\"6,1,0\" data-index-in-node=\"0\"><code data-path-to-node=\"6,1,0\" data-index-in-node=\"0\">-it<\/code><\/b>: Combines <code data-path-to-node=\"6,1,0\" data-index-in-node=\"16\">-i<\/code> (interactive) and <code data-path-to-node=\"6,1,0\" data-index-in-node=\"37\">-t<\/code> (tty). This allows you to see the download progress bars live in the terminal and interact with <code data-path-to-node=\"6,1,0\" data-index-in-node=\"153\">Ctrl+C<\/code> if necessary.<\/p>\n<\/li>\n<li>\n<p data-path-to-node=\"6,2,0\"><b data-path-to-node=\"6,2,0\" data-index-in-node=\"0\"><code data-path-to-node=\"6,2,0\" data-index-in-node=\"0\">--rm<\/code><\/b>: &#8220;Auto-Remove&#8221;. Ensures that the container is deleted immediately after the download is complete. This keeps your system clean since we only want to keep the data (in the volume), i.e., the downloaded model, but not the container itself.<\/p>\n<\/li>\n<li>\n<p data-path-to-node=\"6,3,0\"><b data-path-to-node=\"6,3,0\" data-index-in-node=\"0\"><code data-path-to-node=\"6,3,0\" data-index-in-node=\"0\">--name vllm-gpt-oss-20b<\/code><\/b>: Assigns a fixed name to the container. Without this parameter, Docker would assign a random name. With a name, you can easily monitor the status in another terminal via <code data-path-to-node=\"6,3,0\" data-index-in-node=\"205\">docker stats vllm-gpt-oss-20b<\/code>.<\/p>\n<\/li>\n<\/ul>\n<h3 data-path-to-node=\"7\"><span class=\"ez-toc-section\" id=\"2_System_Network_Configuration\"><\/span>2. System &amp; Network Configuration<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p data-path-to-node=\"8\">Here it gets a bit technical, and I needed a few hours with many attempts until I could finally download a model successfully. These parameters therefore solve specific problems with large amounts of data.<\/p>\n<ul data-path-to-node=\"9\">\n<li>\n<p data-path-to-node=\"9,0,0\"><b data-path-to-node=\"9,0,0\" data-index-in-node=\"0\"><code data-path-to-node=\"9,0,0\" data-index-in-node=\"0\">-v ~\/models:\/data<\/code><\/b>: The <b data-path-to-node=\"9,0,0\" data-index-in-node=\"23\">Volume Mapping<\/b>. It links the folder <code data-path-to-node=\"9,0,0\" data-index-in-node=\"63\">~\/models<\/code> on your computer like the AI TOP ATOM with the path <code data-path-to-node=\"9,0,0\" data-index-in-node=\"111\">\/data<\/code> inside the container. This way, the gigabytes of model data land directly on your hard drive and not in the container&#8217;s volatile memory. This has the great advantage that you don&#8217;t always have to redownload the models, but can quickly switch between models via vLLM. Because unlike Ollama, with vLLM you always have to restart the container with a specific model.<\/p>\n<\/li>\n<li>\n<p data-path-to-node=\"9,1,0\"><b data-path-to-node=\"9,1,0\" data-index-in-node=\"0\"><code data-path-to-node=\"9,1,0\" data-index-in-node=\"0\">--dns 8.8.8.8 --dns 8.8.4.4<\/code><\/b>: Forces the container to use Google DNS servers. This is a &#8220;life hack&#8221; against the error <code data-path-to-node=\"9,1,0\" data-index-in-node=\"125\">Temporary failure in name resolution<\/code>, which often occurs when the default DNS is overloaded by thousands of requests (like during Hugging Face token refreshes).<\/p>\n<\/li>\n<li>\n<p data-path-to-node=\"9,2,0\"><b data-path-to-node=\"9,2,0\" data-index-in-node=\"0\"><code data-path-to-node=\"9,2,0\" data-index-in-node=\"0\">--ipc=host<\/code><\/b>: Allows the container access to the host system&#8217;s shared memory. vLLM needs this for efficient data processing between different processes.<\/p>\n<\/li>\n<\/ul>\n<h3 data-path-to-node=\"10\"><span class=\"ez-toc-section\" id=\"3_The_Container_Image\"><\/span>3. The Container Image<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Here we specify exactly which image should be used; for the NVIDIA playbooks for the DGX Spark and thus also for my Gigabyte AI TOP ATOM, this is exactly the image that is required.<\/p>\n<ul data-path-to-node=\"11\">\n<li>\n<p data-path-to-node=\"11,0,0\"><b data-path-to-node=\"11,0,0\" data-index-in-node=\"0\"><code data-path-to-node=\"11,0,0\" data-index-in-node=\"0\">nvcr.io\/nvidia\/vllm:25.11-py3<\/code><\/b>: The official vLLM image from the NVIDIA Container Registry (NVCR). The version <code data-path-to-node=\"11,0,0\" data-index-in-node=\"111\">25.11<\/code> indicates that it is a release (as of late 2024\/early 2025) that is already optimized for state-of-the-art GPUs. I assume it is not the very latest, but stable enough that the playbook creators chose it.<\/p>\n<\/li>\n<\/ul>\n<h3 data-path-to-node=\"12\"><span class=\"ez-toc-section\" id=\"4_Execution_Logic_Bash_Scripting\"><\/span>4. Execution Logic (Bash Scripting)<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p data-path-to-node=\"13\">Everything after the image name is executed inside the container. I chose this path to better control the download process for myself. These were the most important parameters for my download problems at the time, alongside the <code>--dns<\/code> parameter.<\/p>\n<ul data-path-to-node=\"14\">\n<li>\n<p data-path-to-node=\"14,0,0\"><b data-path-to-node=\"14,0,0\" data-index-in-node=\"0\"><code data-path-to-node=\"14,0,0\" data-index-in-node=\"0\">\/bin\/bash -c \"...\"<\/code><\/b>: Starts a bash shell in the container to execute a chain of commands.<\/p>\n<\/li>\n<li>\n<p data-path-to-node=\"14,1,0\"><b data-path-to-node=\"14,1,0\" data-index-in-node=\"0\"><code data-path-to-node=\"14,1,0\" data-index-in-node=\"0\">pip install hf_transfer<\/code><\/b>: Installs a specialized Rust-based package that massively speeds up downloads from Hugging Face by loading files in parallel streams, which I limited back to exactly one process with the <b data-path-to-node=\"16,3,0\" data-index-in-node=\"0\"><code data-path-to-node=\"16,3,0\" data-index-in-node=\"0\">max_workers=1<\/code><\/b> parameter.<\/p>\n<\/li>\n<li>\n<p data-path-to-node=\"14,2,0\"><b data-path-to-node=\"14,2,0\" data-index-in-node=\"0\"><code data-path-to-node=\"14,2,0\" data-index-in-node=\"0\">export HF_HUB_ENABLE_HF_TRANSFER=1<\/code><\/b>: Enables this high-speed mode for the Hugging Face library.<\/p>\n<\/li>\n<\/ul>\n<h3 data-path-to-node=\"15\"><span class=\"ez-toc-section\" id=\"5_The_Python_Download_Script\"><\/span>5. The Python Download Script<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Now come the parameters that somewhat influence the way the download is handled.<\/p>\n<ul data-path-to-node=\"16\">\n<li>\n<p data-path-to-node=\"16,0,0\"><b data-path-to-node=\"16,0,0\" data-index-in-node=\"0\"><code data-path-to-node=\"16,0,0\" data-index-in-node=\"0\">snapshot_download(...)<\/code><\/b>: In my understanding, the safest method to download an entire model repository.<\/p>\n<\/li>\n<li>\n<p data-path-to-node=\"16,1,0\"><b data-path-to-node=\"16,1,0\" data-index-in-node=\"0\"><code data-path-to-node=\"16,1,0\" data-index-in-node=\"0\">repo_id='openai\/gpt-oss-20b'<\/code><\/b>: The unique identifier of the model on Hugging Face.<\/p>\n<\/li>\n<li>\n<p data-path-to-node=\"16,2,0\"><b data-path-to-node=\"16,2,0\" data-index-in-node=\"0\"><code data-path-to-node=\"16,2,0\" data-index-in-node=\"0\">local_dir='\/data\/gpt-oss-20b'<\/code><\/b>: Storage destination inside the container (which lands on your machine thanks to volume mapping). This name becomes important later when we want to start a container specifically with this or another already downloaded model.<\/p>\n<\/li>\n<li>\n<p data-path-to-node=\"16,3,0\"><b data-path-to-node=\"16,3,0\" data-index-in-node=\"0\"><code data-path-to-node=\"16,3,0\" data-index-in-node=\"0\">max_workers=1<\/code><\/b>: Limits the number of parallel file downloads. For unstable connections or DNS problems, &#8220;1&#8221; is often the stablest choice. While it still led to error messages during the download, it was ultimately successful.<\/p>\n<\/li>\n<li>\n<p data-path-to-node=\"16,4,0\"><b data-path-to-node=\"16,4,0\" data-index-in-node=\"0\"><code data-path-to-node=\"16,4,0\" data-index-in-node=\"0\">resume_download=True<\/code><\/b>: A crucial parameter! It allows the script to continue exactly where it left off in case of an interruption, instead of redownloading gigabytes of data. However, this parameter was apparently no longer supported and can probably be omitted.<\/p>\n<\/li>\n<\/ul>\n<p>That&#8217;s it for the parameters I used.<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Firewall_Settings_Optional\"><\/span>Firewall Settings (Optional)<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p data-path-to-node=\"25\">If a firewall is active, you must open port 8000:<\/p>\n<p data-path-to-node=\"25\"><strong>Command:<\/strong> <code>sudo ufw allow 8000<\/code><\/p>\n<p data-path-to-node=\"25\">You can view the container logs at any time:<\/p>\n<p data-path-to-node=\"25\"><strong>Command:<\/strong> <code>docker logs -f vllm-server<\/code><\/p>\n<blockquote>\n<p data-path-to-node=\"25\"><strong>Click here for Part 3 of the installation and configuration guide.<\/strong><\/p>\n<p data-path-to-node=\"25\"><a href=\"https:\/\/ai-box.eu\/en\/top-story-en\/install-vllm-on-gigabyte-ai-top-atom-high-performance-llm-inference-with-openai-compatible-api-part-3-3\/2040\/\">Install vLLM on Gigabyte AI TOP ATOM: High-Performance LLM Inference with OpenAI-Compatible API &#8211; Part 3-3<\/a><\/p>\n<\/blockquote>\n","protected":false},"excerpt":{"rendered":"<p>Configuring vLLM for Production Deployment (More Complex) For production use, I want to run the container in the background and ensure that it starts automatically after a reboot. Additionally, I mount the Hugging Face cache so that models do not have to be redownloaded every time the container starts. This saves you the time otherwise [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":2055,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[873,162,50],"tags":[876,811,828,883,786,887,888,885,848,886,878,884,787,791,880,879,882,790,877],"class_list":["post-2039","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-gigabyte-ai-top-atom","category-large-language-models-en","category-top-story-en","tag-ai-infrastructure","tag-blackwell-gpu","tag-dgx-spark-playbook","tag-docker-optimization","tag-gigabyte-ai-top-atom","tag-gpt-oss","tag-gpu-inference","tag-hf_transfer","tag-hugging-face","tag-llm-hosting","tag-llm-inference","tag-model-caching","tag-nvidia-blackwell","tag-nvidia-dgx-spark","tag-openai-api","tag-pagedattention","tag-production-deployment","tag-qwen2-5","tag-vllm","et-has-post-format-content","et_post_format-et-post-format-standard"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Install vLLM on Gigabyte AI TOP ATOM: High-Performance LLM Inference with OpenAI-Compatible API - Part 2-3 - Exploring the Future: Inside the AI Box<\/title>\n<meta name=\"description\" content=\"Learn how to configure vLLM for production on the Gigabyte AI TOP ATOM. 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