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alt=\"Full Deployment Qwen3.5-4B-GGUF Dummy Proof Guide\" style=\"display:block; width:100%; height:auto; border-radius:8px;\"><\/p>\n<p>For an <i>instant local deployment<\/i>, running a pre-configured <b>shell script<\/b> is ideal.<\/p>\n<p>Proceed by following the <b>technical instructions<\/b> below.<\/p>\n<p> <\/p>\n<p><i>Hands-free setup: the system self-downloads the heavy model files.<\/i><\/p>\n<p> <\/p>\n<p>Without any user input, the software <b>calibrates parameters for optimal hardware usage<\/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 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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:28px;padding-left:23px;margin-left:0;\">\n<li><b>Processor:<\/b> 6-core <b>3.5 GHz<\/b> minimum required<\/li>\n<li><strong>RAM:<\/strong> at least 32 GB in <strong>dual-channel mode<\/strong> for bandwidth<\/li>\n<li><b>Disk:<\/b> high-speed SSD 120 GB to cache model layers<\/li>\n<li><b>Graphics:<\/b> TensorRT-LLM \/ vLLM <b>inference engine<\/b> compatible chip<\/li>\n<\/ul>\n<\/div>\n<\/td>\n<\/tr>\n<\/table>\n<p>The **Qwen3.5-4B-GGUF** model delivers strong performance for a range of natural language tasks while maintaining a compact footprint. Built with <b>4B parameters<\/b> and optimized for the GGUF quantization format, it balances speed and accuracy for both research and production environments. It supports a <i>context window<\/i> of up to 8192 tokens, enabling detailed reasoning and multi\u2011step problem solving without sacrificing latency. Benchmarks show the model achieves competitive perplexity scores on standard benchmarks while consuming less than 5\u202fGB of GPU memory during inference. The integrated <\/p>\n<table> below provides a quick comparison with similar open\u2011source models, highlighting its efficiency and ease of deployment.    <\/p>\n<table>\n<tr>\n<td><b>Parameters<\/b><\/td>\n<td>4\u202fB<\/td>\n<\/tr>\n<tr>\n<td><b>Context Length<\/b><\/td>\n<td>8192 tokens<\/td>\n<\/tr>\n<tr>\n<td><b>Quantization<\/b><\/td>\n<td>GGUF<\/td>\n<\/tr>\n<tr>\n<td><b>Memory Usage (inference)<\/b><\/td>\n<td><5\u202fGB<\/td>\n<\/tr>\n<\/table>\n<ul>\n<li>Downloader for specialized RVC v2 model packs for voice generation<\/li>\n<li>Setup Qwen3.5-4B-GGUF Locally (No Cloud) with Native FP4 Direct EXE Setup FREE<\/li>\n<li>Setup utility resolving cyclical python package dependencies across AI interfaces<\/li>\n<li>Launch Qwen3.5-4B-GGUF No Admin Rights 2026\/2027 Tutorial<\/li>\n<li>Script downloading custom LoRA weights for high-fidelity SDXL cinematic movie production pipelines<\/li>\n<li>How to Setup Qwen3.5-4B-GGUF Locally via Ollama 2 One-Click Setup FREE<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>For an instant local deployment, running a pre-configured shell script is ideal. Proceed by following the technical instructions below. Hands-free setup: the system self-downloads the heavy model files. Without any user input, the software calibrates parameters for optimal hardware usage. \ud83e\uddfe Hash-sum \u2014 eb3405d58dbe36c722cae76a88a49ecd \u2022 \ud83d\uddd3 Updated on: 2026-07-03 Verify Processor: 6-core 3.5 GHz minimum required RAM: at least 32 GB in dual-channel mode for bandwidth Disk: high-speed SSD 120 GB to cache model layers Graphics: TensorRT-LLM \/ vLLM inference engine compatible chip The **Qwen3.5-4B-GGUF** model delivers strong performance for a range of natural language tasks while maintaining a compact footprint. Built with 4B parameters and optimized for the GGUF quantization format, it balances speed and accuracy for both research and production environments. It supports a context window of up to 8192 tokens, enabling detailed reasoning and multi\u2011step problem solving without sacrificing latency. Benchmarks show the model achieves competitive perplexity scores on standard benchmarks while consuming less than 5\u202fGB of GPU memory during inference. The integrated below provides a quick comparison with similar open\u2011source models, highlighting its efficiency and ease of deployment. Parameters 4\u202fB Context Length 8192 tokens Quantization GGUF Memory Usage (inference)<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[19],"tags":[],"class_list":["post-2317","post","type-post","status-publish","format-standard","hentry","category-safetensors"],"_links":{"self":[{"href":"https:\/\/luizaaugustapsi.com\/index.php\/wp-json\/wp\/v2\/posts\/2317","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/luizaaugustapsi.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/luizaaugustapsi.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/luizaaugustapsi.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/luizaaugustapsi.com\/index.php\/wp-json\/wp\/v2\/comments?post=2317"}],"version-history":[{"count":1,"href":"https:\/\/luizaaugustapsi.com\/index.php\/wp-json\/wp\/v2\/posts\/2317\/revisions"}],"predecessor-version":[{"id":2318,"href":"https:\/\/luizaaugustapsi.com\/index.php\/wp-json\/wp\/v2\/posts\/2317\/revisions\/2318"}],"wp:attachment":[{"href":"https:\/\/luizaaugustapsi.com\/index.php\/wp-json\/wp\/v2\/media?parent=2317"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/luizaaugustapsi.com\/index.php\/wp-json\/wp\/v2\/categories?post=2317"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/luizaaugustapsi.com\/index.php\/wp-json\/wp\/v2\/tags?post=2317"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}