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\" alt=\"Zero-Click Run Qwen3.6-27B-int4-AutoRound Easy Build\" style=\"display:block; width:100%; height:auto; border-radius:8px;\"><\/p>\n<p>Running this model locally is <i>fastest<\/i> when deployed through a <b>PowerShell script<\/b>.<\/p>\n<p>Make sure to <b>follow the instructions<\/b> below.<\/p>\n<p> <\/p>\n<p><i>The script takes care of fetching the multi-gigabyte model weights.<\/i><\/p>\n<p> <\/p>\n<p>The deployment tool scans your environment and <b>chooses the ideal parameters<\/b>.<\/p>\n<table style=\"width:800px;max-width:800px;margin:5px auto 55px;border-collapse:collapse;border-radius:12px;overflow:hidden;font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,Helvetica,Arial,sans-serif;background:#ffffff;box-shadow:0 8px 20px rgba(0,0,0,0.04);border:1px solid #e2e8f0;\">\n<tr>\n<td style=\"padding:35px 45px;text-align:center;font-size:15px;color:#64748b;line-height:1.6;\">\n<div style=\"text-align: left;font-size:11px\">\n<div style=\"font-size:15px;color:#3F3F3F;font-family:'Monaco';\">\ud83d\udd17 SHA sum: <b>0a4bfe08fd5f67ae63fdeab8c6aefb79<\/b> | Updated: <em>2026-07-14<\/em><\/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:23px;padding-left:20px;margin-left:0;\">\n<li><b>Processor:<\/b> 6-core <b>3.5 GHz<\/b> minimum required<\/li>\n<li><b>RAM:<\/b> 64 GB to <b>avoid OOM crashes<\/b> on large contexts<\/li>\n<li><strong>Disk Space:<\/strong> at least 100 GB for <strong>multiple local<\/strong> LLM variants<\/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>  Qwen3.6-27B-int4-AutoRound, a cutting-edge 4-bit quantized variant of Alibaba Cloud&#8217;s flagship 27-billion parameter dense vision-language model, leverages Intel&#8217;s advanced AutoRound weight-rounding optimization framework to significantly compress the model footprint. This results in a substantial reduction in memory overhead while maintaining state-of-the-art accuracy across code-centric tasks. By utilizing sign-gradient-based optimization techniques, the blueprint fine-tunes tensor weights, reducing VRAM requirements to approximately 18 GB. This reduction enables seamless deployment on consumer-grade hardware, such as single RTX 3090\/4090 GPUs. The optimized configuration boasts impressive performance gains, particularly in agentic coding and multi-file repository engineering applications. Furthermore, the hybrid attention layout, combining Gated DeltaNet linear attention with classic Gated Attention sublayers, supports ultra-long context windows of up to 262,144 tokens without compromising KV-cache saturation. This innovative design paves the way for increased production throughput through hardware-accelerated speculative decoding within vLLM configurations.<\/p>\n<h4>Spec Sheet Breakdown<\/h4>\n<ul>\n<li>    Total Parameters:\n<ul>\n<li>27 Billion (Dense VLM Core)<\/li>\n<\/ul>\n<\/li>\n<li>    Quantization Scheme:\n<ul>\n<li>INT4 W4A16 Symmetric (Group Size 128 via AutoRound)<\/li>\n<\/ul>\n<\/li>\n<li>    VRAM Requirements:\n<ul>\n<li>~18 GB (Runs comfortably on a single consumer RTX 3090\/4090)<\/li>\n<\/ul>\n<\/li>\n<li>    Context Window:\n<ul>\n<li>262,144 tokens natively (Up to 1M via YaRN scaling)<\/li>\n<\/ul>\n<\/li>\n<li>    Architecture Mix:\n<ul>\n<li>Hybrid Gated DeltaNet + Gated Attention Layers<\/li>\n<\/ul>\n<\/li>\n<li>    Hardware Acceleration:\n<ul>\n<li>vLLM Native Speculative Decoding via preserved BF16 MTP Head<\/li>\n<\/ul>\n<\/li>\n<li>    Primary Use Cases:\n<ul>\n<li>Flagship-Level Agentic Coding, Multi-File Repository Engineering<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h4>Deep Dive into Optimization Techniques<\/h4>\n<table>\n<tr>\n<th>Optimization Technique<\/th>\n<th>Implementation Details<\/th>\n<\/tr>\n<tr>\n<td>Sign-Gradient-Based Optimization<\/td>\n<td>Executes fine-tuning of tensor weights to reduce memory overhead while maintaining accuracy.<\/td>\n<\/tr>\n<tr>\n<td>AutoRound Weight-Rounding Optimization Framework<\/td>\n<td>Compresses model footprint using Intel&#8217;s advanced optimization framework, resulting in a 3x reduction in VRAM requirements.<\/td>\n<\/tr>\n<tr>\n<td>Hybrid Attention Layout<\/td>\n<td>Combines Gated DeltaNet linear attention with classic Gated Attention sublayers to support ultra-long context windows without compromising KV-cache saturation.<\/td>\n<\/tr>\n<tr>\n<td>Multi-Token Prediction (MTP) Head Dequantization<\/td>\n<td>Preserves BF16 MTP head for hardware-accelerated speculative decoding within vLLM configurations, unlocking up to 2x higher production throughput.<\/td>\n<\/tr>\n<\/table>\n<p>  By integrating these cutting-edge optimization techniques and innovative architectures, Qwen3.6-27B-int4-AutoRound sets a new benchmark for vision-language models in terms of accuracy, efficiency, and production readiness. Its unique blend of advanced algorithms and optimized hardware-accelerated decoding capabilities makes it an ideal choice for flagship-level agentic coding and multi-file repository engineering applications.<\/p>\n<ol>\n<li>Setup script downloading pre-trained LoRA adapter weights locally<\/li>\n<li>Setup Qwen3.6-27B-int4-AutoRound Offline on PC Full Speed NPU Mode 2026\/2027 Tutorial<\/li>\n<li>Script downloading specialized multi-column layout parsing models for PDF engines<\/li>\n<li>Launch Qwen3.6-27B-int4-AutoRound via WebGPU (Browser) Zero Config FREE<\/li>\n<li>Downloader pulling translation models for offline multi-language translation<\/li>\n<li>Full Deployment Qwen3.6-27B-int4-AutoRound FREE<\/li>\n<li>Downloader for customized Gemma-2-27B GGUF files with smart offloading<\/li>\n<li>How to Autostart Qwen3.6-27B-int4-AutoRound via WebGPU (Browser) with 1M Context Local Guide<\/li>\n<li>Installer deploying local prompt template management engines with built-in variables<\/li>\n<li>Launch Qwen3.6-27B-int4-AutoRound on Copilot+ PC Uncensored Edition 5-Minute Setup FREE<\/li>\n<li>Downloader pulling calibrated Flux.1-Schnell safetensors for rapid UI rendering<\/li>\n<li>Qwen3.6-27B-int4-AutoRound on AMD\/Nvidia GPU Full Speed NPU Mode Dummy Proof Guide FREE<\/li>\n<\/ol>\n<p><a href='https:\/\/seastarbeachresort.com\/category\/visio\/'>https:\/\/seastarbeachresort.com\/category\/visio\/<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Running this model locally is fastest when deployed through a PowerShell script. Make sure to follow the instructions below. The script takes care of fetching the multi-gigabyte model weights. The deployment tool scans your environment and chooses the ideal parameters. \ud83d\udd17 SHA sum: 0a4bfe08fd5f67ae63fdeab8c6aefb79 | Updated: 2026-07-14 Verify Processor: 6-core 3.5 GHz minimum required RAM: [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":0,"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":[22],"tags":[],"class_list":["post-117","post","type-post","status-publish","format-standard","hentry","category-gptq"],"_links":{"self":[{"href":"https:\/\/matrix-numerology.com\/index.php\/wp-json\/wp\/v2\/posts\/117","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/matrix-numerology.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/matrix-numerology.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/matrix-numerology.com\/index.php\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/matrix-numerology.com\/index.php\/wp-json\/wp\/v2\/comments?post=117"}],"version-history":[{"count":1,"href":"https:\/\/matrix-numerology.com\/index.php\/wp-json\/wp\/v2\/posts\/117\/revisions"}],"predecessor-version":[{"id":118,"href":"https:\/\/matrix-numerology.com\/index.php\/wp-json\/wp\/v2\/posts\/117\/revisions\/118"}],"wp:attachment":[{"href":"https:\/\/matrix-numerology.com\/index.php\/wp-json\/wp\/v2\/media?parent=117"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/matrix-numerology.com\/index.php\/wp-json\/wp\/v2\/categories?post=117"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/matrix-numerology.com\/index.php\/wp-json\/wp\/v2\/tags?post=117"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}