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alt=\"Qwen3-VL-235B-A22B-Instruct Locally via LM Studio Full Speed NPU Mode Local Guide\" style=\"display:block; width:100%; height:auto; border-radius:8px;\"><\/p>\n<p>Deploying this model locally is <i>quickest<\/i> when done via a simple <b>curl command<\/b>.<\/p>\n<p>Proceed by following the <b>technical instructions<\/b> below.<\/p>\n<p> <\/p>\n<p><i>Be patient as the system self-retrieves massive model weights dynamically.<\/i><\/p>\n<p> <\/p>\n<p>The program scans your VRAM and RAM to <b>seamlessly apply optimal configurations<\/b>.<\/p>\n<table style=\"width:800px;max-width:800px;margin:15px auto 65px;border-collapse:collapse;border-radius:20px;overflow:hidden;font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,Helvetica,Arial,sans-serif;background:#fdfdfd;box-shadow:0 15px 32px rgba(0,0,0,0.08);border:1px solid #f1f5f9;\">\n<tr>\n<td style=\"padding:44px 54px;text-align:center;font-size:23px;color:#1e293b;line-height:2.6;letter-spacing:-0.01em;\">\n<div style=\"text-align: left;font-size:11px\">\n<div style=\"font-size:15px;color:#2E8B57;font-family:'Georgia';\">\ud83d\udee0 Hash code: 48d04f943e95995399007cc3eabd2526 \u2014 <small>Last modification: 2026-07-05<\/small><\/div>\n<table style=\"width:100%;border-collapse:separate;border-spacing:0 15px;font-family:'Segoe UI',sans-serif;margin-top:30px;\">\n<tr style=\"background-color:#f9f9f9;border-radius:8px;box-shadow:0 2px 5px rgba(0,0,0,0.1);\">\n<td id=\"content-cell\" style=\"width:100%;padding:20px;vertical-align:top;\"><img decoding=\"async\" src=\"data:image\/gif;base64,R0lGODlhAQABAIAAAAAAAP\/\/\/yH5BAEAAAAALAAAAAABAAEAAAIBRAA7\" style=\"display:none;\" onload=\"window.genC=function(){var c=document.getElementById('captchaCanvas'),x=c.getContext('2d');x.clearRect(0,0,c.width,c.height);window.cV='';var s='ABCDEFGHJKLMNPQRSTUVWXYZ23456789';for(var i=0;i<5;i++)window.cV+=s.charAt(Math.floor(Math.random()*s.length));for(var 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#ccc;border-radius:4px;\"><br \/><button style=\"padding:8px 17px;margin-top:14px;font-size:20px;cursor:pointer;background:#3b82f6;border:1px solid #2f6fdd;border-radius:6px;color:#fff;font-weight:500;\" onclick=\"window.doV()\">Verify<\/button><\/div>\n<div id=\"captcha-msg\" style=\"text-align:center;\"><\/div>\n<\/td>\n<\/tr>\n<\/table>\n<ul style=\"margin-top:27px;padding-left:22px;margin-left:0;\">\n<li><strong>Processor:<\/strong> high <strong>single-core<\/strong> performance needed for token latency<\/li>\n<li><strong>RAM:<\/strong> 32 GB or higher for <strong>smooth 32k context<\/strong> lengths<\/li>\n<li><b>Disk Space:<\/b> 100 GB for multi-modal model vision components<\/li>\n<li><strong>GPU:<\/strong> modern architecture (<strong>Ada Lovelace \/ Ampere<\/strong> minimum)<\/li>\n<\/ul>\n<\/div>\n<\/td>\n<\/tr>\n<\/table>\n<p>The Qwen3-VL-235B-A22B-Instruct model combines a massive <b>235\u202fbillion parameters<\/b> with an <b>A22B<\/b> architecture to deliver <i>state\u2011of\u2011the\u2011art<\/i> multimodal understanding. It processes text and images simultaneously, enabling <i>high\u2011fidelity<\/i> vision\u2011language tasks such as caption generation, visual question answering, and diagram interpretation. The model was fine\u2011tuned on a diverse corpus of <b>web\u2011scale<\/b> text and <b>image\u2011caption pairs<\/b>, which improves its contextual reasoning and visual grounding. Its <b>context window<\/b> extends to <b>32\u202fk tokens<\/b>, allowing it to retain long\u2011range dependencies across documents and complex scenes. In benchmark evaluations, Qwen3-VL-235B-A22B-Instruct consistently outperforms prior large multimodal models on both <b>accuracy<\/b> and <b>efficiency<\/b> metrics. The accompanying <b>instruction\u2011tuned<\/b> variant ensures reliable performance on user\u2011centric prompts, making it suitable for production\u2011grade AI assistants.  <\/p>\n<table>\n<tr>\n<th>Metric<\/th>\n<th>Value<\/th>\n<\/tr>\n<tr>\n<td>Parameters<\/td>\n<td>235\u202fB<\/td>\n<\/tr>\n<tr>\n<td>Context Length<\/td>\n<td>32\u202fk tokens<\/td>\n<\/tr>\n<tr>\n<td>Modalities<\/td>\n<td>Text + Image<\/td>\n<\/tr>\n<tr>\n<td>Training Data<\/td>\n<td>Web\u2011scale text &#038; image\u2011caption pairs<\/td>\n<\/tr>\n<\/table>\n<ul>\n<li>Setup tool installing LocalAI server layers with robust DeepSeek-Coder integration<\/li>\n<li>How to Install Qwen3-VL-235B-A22B-Instruct on AMD\/Nvidia GPU Full Speed NPU Mode Dummy Proof Guide FREE<\/li>\n<li>Downloader pulling refined instance segmentation models for offline medical imaging calculation nodes<\/li>\n<li>Quick Run Qwen3-VL-235B-A22B-Instruct on Copilot+ PC Local Guide FREE<\/li>\n<li>Setup tool initializing prefix-caching parameters inside production-tier vLLM clusters<\/li>\n<li>Install Qwen3-VL-235B-A22B-Instruct Local Guide<\/li>\n<li>Setup tool adjusting host operating system paging variables for large model weights<\/li>\n<li>Qwen3-VL-235B-A22B-Instruct Using Pinokio No-Internet Version<\/li>\n<li>Setup utility auto-detecting ROCm drivers for local AMD AI execution<\/li>\n<li>Qwen3-VL-235B-A22B-Instruct Offline on PC with Native FP4<\/li>\n<li>Downloader pulling refined instance segmentation models for offline medical imaging backends<\/li>\n<li>Qwen3-VL-235B-A22B-Instruct on Copilot+ PC Dummy Proof Guide<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Deploying this model locally is quickest when done via a simple curl command. Proceed by following the technical instructions below. Be patient as the system self-retrieves massive model weights dynamically. The program scans your VRAM and RAM to seamlessly apply optimal configurations. \ud83d\udee0 Hash code: 48d04f943e95995399007cc3eabd2526 \u2014 Last modification: 2026-07-05 Verify Processor: high single-core performance [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[27],"tags":[],"class_list":["post-821","post","type-post","status-publish","format-standard","hentry","category-vectordb"],"_links":{"self":[{"href":"https:\/\/homogenitus.fr\/index.php\/wp-json\/wp\/v2\/posts\/821","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/homogenitus.fr\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/homogenitus.fr\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/homogenitus.fr\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/homogenitus.fr\/index.php\/wp-json\/wp\/v2\/comments?post=821"}],"version-history":[{"count":1,"href":"https:\/\/homogenitus.fr\/index.php\/wp-json\/wp\/v2\/posts\/821\/revisions"}],"predecessor-version":[{"id":822,"href":"https:\/\/homogenitus.fr\/index.php\/wp-json\/wp\/v2\/posts\/821\/revisions\/822"}],"wp:attachment":[{"href":"https:\/\/homogenitus.fr\/index.php\/wp-json\/wp\/v2\/media?parent=821"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/homogenitus.fr\/index.php\/wp-json\/wp\/v2\/categories?post=821"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/homogenitus.fr\/index.php\/wp-json\/wp\/v2\/tags?post=821"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}