--- title: Optimizing Local LLMs for Low-End Hardware: 8GB GPU Guide description: Running local LLMs on 8GB GPUs is practical with the right model selection and quantization. A 7B–8B parameter model at Q4_K_M quantization fits within 5GB... canonical: https://daily.dev/posts/optimizing-local-llms-for-low-end-hardware-8gb-gpu-guide-h3kyjc7i7 twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Optimizing Local LLMs for Low-End Hardware: 8GB GPU Guide | daily.dev og:description: Running local LLMs on 8GB GPUs is practical with the right model selection and quantization. A 7B–8B parameter model at Q4_K_M quantization fits within 5GB... og:url: https://daily.dev/posts/optimizing-local-llms-for-low-end-hardware-8gb-gpu-guide-h3kyjc7i7 og:image: https://api.daily.dev/og/posts/H3Kyjc7i7.png og:image:alt: Optimizing Local LLMs for Low-End Hardware: 8GB GPU Guide og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Optimizing Local LLMs for Low-End Hardware: 8GB GPU Guide **[SitePoint](https://daily.dev/sources/sitepoint)** · 19 min read · 0 upvotes · 0 comments ## Summary Running local LLMs on 8GB GPUs is practical with the right model selection and quantization. A 7B–8B parameter model at Q4_K_M quantization fits within 5GB VRAM, leaving room for KV cache. The guide covers GGUF quantization formats (Q4_K_M, Q5_K_M, Q8_0), model recommendations per VRAM tier (8GB, 6GB, 4GB), and step-by-step setup for both Ollama and llama.cpp. Key optimizations include reducing context window from 4096 to 2048 tokens (freeing 1–2GB VRAM), explicit quantization tag selection when pulling models, GPU layer offloading via num_gpu/ngl flags, and partial CPU offloading for 14B models. Common pitfalls include pulling default FP16 model tags, setting maximum context lengths, thermal throttling on older cards, and unrealistic quality expectations from small quantized models. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [How much VRAM do you need to run an LLM locally?](https://daily.dev/posts/how-much-vram-do-you-need-to-run-an-llm-locally--sjxd57zfc) · Flavio Copes · 2 upvotes · 1 comments - [Your old GPU can still run big LLMs – you just need the right tweaks](https://daily.dev/posts/your-old-gpu-can-still-run-big-llms-you-just-need-the-right-tweaks-3sa1xwzxu) · XDA Developers · 0 upvotes · 0 comments - [10GB VRAM Local LLM: The Complete Setup Guide \(2026\)](https://daily.dev/posts/10gb-vram-local-llm-the-complete-setup-guide-2026--0um356pje) · SitePoint · 0 upvotes · 0 comments - [How to Run LLMs Locally: A Practical Guide for Developers](https://daily.dev/posts/how-to-run-llms-locally-a-practical-guide-for-developers-tijurupob) · BigData Boutique blog · 2 upvotes · 0 comments - [I almost upgraded my GPU to run larger local LLMs, but this 8B model proved I didn't have to](https://daily.dev/posts/i-almost-upgraded-my-gpu-to-run-larger-local-llms-but-this-8b-model-proved-i-didn-t-have-to-gc24e505l) · XDA Developers · 1 upvotes · 0 comments --- Tags: [#data-science](https://daily.dev/tags/data-science), [#llama-cpp](https://daily.dev/tags/llama-cpp), [#local-ai](https://daily.dev/tags/local-ai), [#ollama](https://daily.dev/tags/ollama) [View this post on daily.dev](https://daily.dev/posts/optimizing-local-llms-for-low-end-hardware-8gb-gpu-guide-h3kyjc7i7) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Optimizing Local LLMs for Low-End Hardware: 8GB GPU Guide","url":"https://daily.dev/posts/optimizing-local-llms-for-low-end-hardware-8gb-gpu-guide-h3kyjc7i7","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/optimizing-local-llms-for-low-end-hardware-8gb-gpu-guide-h3kyjc7i7"},"datePublished":"2026-03-05T14:35:32.122Z","dateModified":"2026-05-14T02:13:15.240Z","description":"Running local LLMs on 8GB GPUs is practical with the right model selection and quantization. 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