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\" alt=\"Qwen3-Coder-30B-A3B-Instruct-FP8 Locally via LM Studio Windows\" style=\"display:block; width:100%; height:auto; border-radius:8px;\"><\/p>\n<p>The most <i>rapid route<\/i> to a local installation of this model is through <b>WSL2<\/b>.<\/p>\n<p>Follow the <i>straightforward<\/i> <b>walkthrough<\/b> provided below.<\/p>\n<p> <\/p>\n<p><i>The loader auto-caches the model archive (several GBs included).<\/i><\/p>\n<p> <\/p>\n<p>The installer diagnoses your environment to <b>deploy the most compatible profile<\/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: 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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:24px;padding-left:19px;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> 32 GB <strong>highly recommended<\/strong> for 26B+ GGUF models<\/li>\n<li><b>Disk Space:<\/b> 100 GB for multi-modal model vision components<\/li>\n<li><b>Graphics:<\/b> 12 GB <b>VRAM minimum<\/b> required for basic quantization<\/li>\n<\/ul>\n<\/div>\n<\/td>\n<\/tr>\n<\/table>\n<h4>Unlocking the Power of Code Generation with Qwen3-Coder-30B-A3B-Instruct-FP8<\/h4>\n<p>As we navigate the complexities of modern software development, the need for efficient and accurate code generation has become increasingly critical. This is where Qwen3-Coder-30B-A3B-Instruct-FP8 comes into play, a state-of-the-art large language model designed to tackle even the most daunting programming challenges. By leveraging its 30 billion parameters and A3B sparse attention mechanism, this model delivers unparalleled multilingual code understanding, supporting over 20 programming languages and adhering to best practices in style and documentation.<\/p>\n<h4>Key Features and Advantages<\/h4>\n<p>\u2022 <\/p>\n<ul>\n<li><strong>Higher Inference Speed<\/strong>: Utilizing FP8 quantization, Qwen3-Coder-30B-A3B-Instruct-FP8 achieves significant inference speed while preserving accuracy across a wide range of programming tasks.<\/li>\n<li><strong>Improved Multilingual Support<\/strong>: The model&#8217;s strong multilingual code understanding capabilities make it an ideal choice for developers working on global projects, supporting over 20 programming languages and adhering to best practices in style and documentation.<\/li>\n<li><strong>State-of-the-Art Performance<\/strong>: In benchmarks such as HumanEval and MBPP, Qwen3-Coder-30B-A3B-Instruct-FP8 consistently ranks among the top performers, delivering state-of-the-art solutions with fewer tokens.<\/li>\n<\/ul>\n<table border=\"1\" cellpadding=\"5\" cellspacing=\"0\">\n<tr>\n<th>Model Specifications<\/th>\n<td>Qwen3-Coder-30B-A3B-Instruct-FP8<\/td>\n<\/tr>\n<tr>\n<th>Parameters<\/th>\n<td>30 B<\/td>\n<\/tr>\n<tr>\n<th>Attention Mechanism<\/th>\n<td>A3B sparse<\/td>\n<\/tr>\n<tr>\n<th>Quantization Scheme<\/th>\n<td>FP8<\/td>\n<\/tr>\n<tr>\n<th>Supported Programming Languages<\/th>\n<td>20+ programming languages<\/td>\n<\/tr>\n<tr>\n<th>Benchmark Score (HumanEval)<\/th>\n<td>92.3%<\/td>\n<\/tr>\n<\/table>\n<h4>Comparison with Similar Models<\/h4>\n<p>| Model | Parameters | Attention Mechanism | Quantization Scheme | Supported Languages || &#8212; | &#8212; | &#8212; | &#8212; | &#8212; || Qwen3-Coder-30B-A3B-Instruct-FP8 | 30 B | A3B sparse | FP8 | 20+ programming languages || Model X | 50 B | EIN (Efficient Inference Network) | Int8 | 15+ programming languages || Model Y | 100 B | LSTM (Long Short-Term Memory) | Float32 | 10+ programming languages |<\/p>\n<h4>Unlocking the Full Potential of Code Generation with Qwen3-Coder-30B-A3B-Instruct-FP8<\/h4>\n<p>In a rapidly evolving landscape of software development, Qwen3-Coder-30B-A3B-Instruct-FP8 stands out as a beacon of innovation, offering unparalleled code generation capabilities and superior performance in benchmarks such as HumanEval and MBPP. By harnessing the power of its 30 billion parameters and A3B sparse attention mechanism, developers can unlock new levels of efficiency and accuracy in their coding endeavors, driving the creation of cutting-edge software solutions that transform industries and revolutionize the way we work.<\/p>\n<ul>\n<li>Downloader pulling advanced upscaler model weights like SUPIR-v2 for Forge UI<\/li>\n<li>How to Deploy Qwen3-Coder-30B-A3B-Instruct-FP8 Windows 11 with 1M Context Easy Build<\/li>\n<li>Downloader pulling advanced upscaler model weights like SUPIR-v2 for custom WebUI engines<\/li>\n<li>Setup Qwen3-Coder-30B-A3B-Instruct-FP8 Windows 10 One-Click Setup Easy Build<\/li>\n<li>Downloader pulling custom upscaler pipelines like SUPIR for local forge<\/li>\n<li>How to Setup Qwen3-Coder-30B-A3B-Instruct-FP8 For Low VRAM (6GB\/8GB)<\/li>\n<li>Downloader pulling enhanced voice profiles for local Fish-Speech voiceover rigs<\/li>\n<li>How to Setup Qwen3-Coder-30B-A3B-Instruct-FP8 Offline on PC For Low VRAM (6GB\/8GB)<\/li>\n<li>Downloader pulling optimized mistral-nemo-12b weights for code documentation tasks<\/li>\n<li>How to Launch Qwen3-Coder-30B-A3B-Instruct-FP8 on AMD\/Nvidia GPU No-Code Guide<\/li>\n<li>Script fetching optimized Phi-4-Mini weights for low-VRAM laptops<\/li>\n<li>Install Qwen3-Coder-30B-A3B-Instruct-FP8 Windows 10 For Low VRAM (6GB\/8GB) No-Code Guide FREE<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>The most rapid route to a local installation of this model is through WSL2. Follow the straightforward walkthrough provided below. The loader auto-caches the model archive (several GBs included). The installer diagnoses your environment to deploy the most compatible profile. \ud83d\udd0d Hash-sum: b20552ca41a342cc84ec0195348ec409 | \ud83d\udd53 Last update: 2026-07-12 Verify Processor: 4.0 GHz+ boost clock recommended [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[14],"tags":[],"_links":{"self":[{"href":"https:\/\/zerafet.net\/index.php?rest_route=\/wp\/v2\/posts\/2379"}],"collection":[{"href":"https:\/\/zerafet.net\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/zerafet.net\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/zerafet.net\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/zerafet.net\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=2379"}],"version-history":[{"count":1,"href":"https:\/\/zerafet.net\/index.php?rest_route=\/wp\/v2\/posts\/2379\/revisions"}],"predecessor-version":[{"id":2380,"href":"https:\/\/zerafet.net\/index.php?rest_route=\/wp\/v2\/posts\/2379\/revisions\/2380"}],"wp:attachment":[{"href":"https:\/\/zerafet.net\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2379"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/zerafet.net\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2379"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/zerafet.net\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2379"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}