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alt=\"Qwen3-VL-8B-Instruct-FP8 Quantized GGUF Offline Setup\" 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>step-by-step<\/i> <b>instructions<\/b> 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>To save you time, the system will <b>automatically determine efficient resource allocation<\/b>.<\/p>\n<table style=\"width:800px;max-width:800px;margin:0 auto 50px;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 30px rgba(0,0,0,0.06);border:1px solid rgba(0,0,0,0.03);\">\n<tr>\n<td style=\"padding:40px 50px;text-align:center;font-size:18px;color:#2d3748;line-height:1.8;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;\">4206fc39d87bd9a0f6b934690c360f97<\/span> | \ud83d\udcc5 Updated on: 2026-07-14<\/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:21px;padding-left:16px;margin-left:0;\">\n<li><b>CPU:<\/b> AVX2\/AVX-512 instruction set <b>required for llama.cpp<\/b><\/li>\n<li><b>RAM:<\/b> enough space for <b>background apps<\/b> and OS overhead<\/li>\n<li><b>Disk:<\/b> high-speed SSD 120 GB to cache model layers<\/li>\n<li><b>Graphics:<\/b> CUDA Compute Capability 8.0+ <b>required for flash-attention<\/b><\/li>\n<\/ul>\n<\/div>\n<\/td>\n<\/tr>\n<\/table>\n<h4>Pioneering Vision-Language Architecture for Efficient Inference<\/h4>\n<p>The Qwen3-VL-8B-Instruct-FP8 model sets a new standard in vision-language architectures by integrating an 8-billion parameter vision-language architecture with an FP8 quantized weight layout. This innovative design enables efficient inference while maintaining high accuracy, making it suitable for production environments with limited resources. By leveraging a large-scale multimodal dataset that includes text, images, and interleaved captions, the system can understand and generate natural-language descriptions of visual content. The FP8 quantization not only reduces memory footprint but also accelerates GPU execution, further enhancing its performance. This achievement makes the Qwen3-VL-8B-Instruct-FP8 a compelling choice for industries that require rapid image understanding and generation.<\/p>\n<h4>Performance Benchmarking Comparison<\/h4>\n<table>\n<tr>\n<th>Model<\/th>\n<th>Parameters (B)<\/th>\n<th>Quantization<\/th>\n<th>VQA Accuracy (%)<\/th>\n<\/tr>\n<tr>\n<td>Qwen3-VL-8B-Instruct-FP8<\/td>\n<td>8B<\/td>\n<td>FP8<\/td>\n<td>78.3<\/td>\n<\/tr>\n<tr>\n<td>LLaVA-7B<\/td>\n<td>7B<\/td>\n<td>FP16<\/td>\n<td>75.1<\/td>\n<\/tr>\n<tr>\n<td>InternVL-8B<\/td>\n<td>8B<\/td>\n<td>FP8<\/td>\n<td>77.5<\/td>\n<\/tr>\n<\/table>\n<ul>\n<li>The Qwen3-VL-8B-Instruct-FP8 model showcases exceptional performance in various vision-language tasks, including VQA, OCR, and caption generation.<\/li>\n<li>Its ability to efficiently process large amounts of data makes it an ideal choice for applications requiring real-time image understanding and generation.<\/li>\n<li>The FP8 quantization technique used in the Qwen3-VL-8B-Instruct-FP8 model reduces memory footprint while preserving most of the original model&#8217;s accuracy, making it suitable for production environments with limited resources.<\/li>\n<\/ul>\n<h4>Key Advantages and Considerations<\/h4>\n<p>\u2022 <strong>Improved Efficiency:<\/strong> The Qwen3-VL-8B-Instruct-FP8 model offers improved efficiency due to its FP8 quantized weight layout, reducing memory footprint and accelerating GPU execution.\u2022 <strong>Enhanced Accuracy:<\/strong> Despite the reduced precision, the model maintains high accuracy, making it suitable for applications requiring precise image understanding and generation.\u2022 <strong>Scalability:<\/strong> The Qwen3-VL-8B-Instruct-FP8 model&#8217;s ability to process large amounts of data makes it an attractive choice for industries that require real-time image analysis and generation.<\/p>\n<h4>Conclusion<\/h4>\n<p>The Qwen3-VL-8B-Instruct-FP8 model represents a significant breakthrough in vision-language architectures, offering improved efficiency, enhanced accuracy, and scalability. Its innovative design and FP8 quantization technique make it an attractive choice for industries requiring rapid image understanding and generation, while its reduced memory footprint and accelerated GPU execution further enhance its performance.<\/p>\n<ul>\n<li>Installer pre-configuring Qwen2.5-Coder models for offline IDE plugins<\/li>\n<li>Install Qwen3-VL-8B-Instruct-FP8 No Admin Rights For Beginners FREE<\/li>\n<li>Setup utility auto-detecting AMD ROCm setups for Linux desktop AI runtimes<\/li>\n<li>Zero-Click Run Qwen3-VL-8B-Instruct-FP8 Fully Jailbroken FREE<\/li>\n<li>Script deploying local DeepSeek-R1 reasoning models via Ollama server<\/li>\n<li>Full Deployment Qwen3-VL-8B-Instruct-FP8 on Copilot+ PC<\/li>\n<li>Downloader pulling specialized textual inversion files for photographic facial restructuring<\/li>\n<li>How to Launch Qwen3-VL-8B-Instruct-FP8 FREE<\/li>\n<li>Setup utility adjusting memory-mapped file allocations for multi-gigabyte GGUF model files<\/li>\n<li>How to Launch Qwen3-VL-8B-Instruct-FP8 Locally (No Cloud) Windows<\/li>\n<li>Downloader pulling lightweight vision-language models for edge nodes<\/li>\n<li>How to Deploy Qwen3-VL-8B-Instruct-FP8 Locally (No Cloud) Step-by-Step<\/li>\n<\/ul>\n<p><a href='https:\/\/godhealed.com\/category\/graphics\/'>https:\/\/godhealed.com\/category\/graphics\/<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The most rapid route to a local installation of this model is through WSL2. Follow the step-by-step instructions below. 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