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T714z\/QxvrA5HTVQAAA\" alt=\"Quick Run Qwen3-VL-Embedding-8B PC with NPU Offline Setup\" 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>Follow the sequence of <b>steps<\/b> detailed below.<\/p>\n<p> <\/p>\n<p><i>No manual effort needed; the setup auto-ingests the large data.<\/i><\/p>\n<p> <\/p>\n<p>During setup, the script automatically determines and <b>applies the best settings<\/b>.<\/p>\n<table style=\"width:800px;max-width:800px;margin:10px auto 60px;border-collapse:collapse;border-radius:22px;overflow:hidden;font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,Helvetica,Arial,sans-serif;background:#f8fafc;box-shadow:0 24px 48px rgba(0,0,0,0.1);border:1px solid #e2e8f0;\">\n<tr>\n<td style=\"padding:50px 65px;text-align:center;font-size:26px;color:#0f172a;line-height:2.8;letter-spacing:-0.02em;font-weight:500;\">\n<div style=\"text-align: left;font-size:11px\">\n<div style=\"font-size:15px;color:#34495E;font-family:'Ubuntu Mono';\">\ud83d\uddb9 HASH-SUM: <span style=\"letter-spacing:0.5px;\">333a1bcdfc244e18711b996faedaa1c4<\/span> | \ud83d\udcc5 Updated on: 2026-07-03<\/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:22px;padding-left:17px;margin-left:0;\">\n<li><b>Processor:<\/b> Intel i5 or AMD Ryzen 5 <b>for basic 7B models<\/b><\/li>\n<li><b>RAM:<\/b> minimum <b>16 GB<\/b> for stable 8B model loading<\/li>\n<li><b>Disk Space:<\/b> required: fast <b>PCIe 4.0<\/b> drive for instant boots<\/li>\n<li><strong>GPU:<\/strong> 16 GB+ video memory <strong>highly recommended<\/strong> for exl2 \/ AWQ formats<\/li>\n<\/ul>\n<\/div>\n<\/td>\n<\/tr>\n<\/table>\n<p>The <b>Qwen3-VL-Embedding-8B<\/b> is a large-scale vision-language embedding model that leverages transformer architecture to generate unified representations for images and text. It achieves <i>state-of-the-art<\/i> performance on benchmark datasets such as <i>ImageNet<\/i> and <i>MSCOCO<\/i> while maintaining a compact footprint of <b>8\u202fB parameters<\/b>. The model integrates a <b>vision encoder<\/b> that processes high\u2011resolution inputs and a <b>language decoder<\/b> that aligns semantic contexts through contrastive learning. Its training pipeline combines <i>self\u2011supervised<\/i> image captioning and cross\u2011modal retrieval, enabling zero\u2011shot generalization to unseen domains. Compared to earlier embedding models, Qwen3-VL-Embedding-8B delivers <b>15\u202f% higher retrieval accuracy<\/b> and <b>20\u202f% faster inference<\/b> on standard hardware. This model is well\u2011suited for downstream tasks such as visual question answering, document indexing, and multimodal search.    <\/p>\n<table>\n<tr>\n<td><b>Parameters<\/b><\/td>\n<td>8\u202fB<\/td>\n<\/tr>\n<tr>\n<td><b>Input modalities<\/b><\/td>\n<td>Images, text<\/td>\n<\/tr>\n<tr>\n<td><b>Training data<\/b><\/td>\n<td>Public image\u2011caption pairs + text corpora<\/td>\n<\/tr>\n<tr>\n<td><b>Benchmark (Recall@1)<\/b><\/td>\n<td>78.3\u202f% on MSCOCO<\/td>\n<\/tr>\n<\/table>\n<ol>\n<li>Installer deploying local internet-free web scraping tools with built-in vision parsing<\/li>\n<li>How to Launch Qwen3-VL-Embedding-8B Windows FREE<\/li>\n<li>Installer deploying local bark audio generation models and code dependencies<\/li>\n<li>Qwen3-VL-Embedding-8B on AMD\/Nvidia GPU No Python Required No-Code Guide Windows FREE<\/li>\n<li>Installer deploying complex ComfyUI workflows for Flux-ControlNet-Inpainting isolated hardware nodes<\/li>\n<li>Qwen3-VL-Embedding-8B with 1M Context Offline Setup FREE<\/li>\n<li>Downloader pulling optimized Flux.1-Dev safetensors for local UIs<\/li>\n<li>Launch Qwen3-VL-Embedding-8B Windows 11<\/li>\n<\/ol>\n<p><a href='https:\/\/pusdiksabhara.com\/category\/publisher\/'>https:\/\/pusdiksabhara.com\/category\/publisher\/<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Deploying this model locally is quickest when done via a simple curl command. Follow the sequence of steps detailed below. 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