--- title: Quantization Explained: Run 70B Models on Consumer GPUs description: Running 70B parameter language models on consumer GPUs requires quantization to reduce memory footprint. This deep dive covers the major quantization... canonical: https://daily.dev/posts/quantization-explained-run-70b-models-on-consumer-gpus-2lmh0n2hc twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Quantization Explained: Run 70B Models on Consumer GPUs | daily.dev og:description: Running 70B parameter language models on consumer GPUs requires quantization to reduce memory footprint. This deep dive covers the major quantization... og:url: https://daily.dev/posts/quantization-explained-run-70b-models-on-consumer-gpus-2lmh0n2hc og:image: https://api.daily.dev/og/posts/2lmh0N2HC.png og:image:alt: Quantization Explained: Run 70B Models on Consumer GPUs 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. # Quantization Explained: Run 70B Models on Consumer GPUs **[SitePoint](https://daily.dev/sources/sitepoint)** · 15 min read · 0 upvotes · 0 comments ## Summary Running 70B parameter language models on consumer GPUs requires quantization to reduce memory footprint. This deep dive covers the major quantization formats—GGUF, EXL2, GPTQ, and AWQ—explaining their tradeoffs in speed, quality, and VRAM requirements. A VRAM estimation formula and Python/JavaScript calculator are provided, along with step-by-step instructions for running 70B models using llama.cpp (with CPU layer offloading) and ExLlamaV2. Perplexity benchmarks show Q4_K_M loses only ~1.5% quality vs FP16, making it the practical default for single-GPU setups. EXL2 is 2–3× faster when the full model fits in VRAM, but GGUF with CPU offloading is the only viable path for 70B models on a single 24GB card. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [Optimizing Local LLMs for Low-End Hardware: 8GB GPU Guide](https://daily.dev/posts/optimizing-local-llms-for-low-end-hardware-8gb-gpu-guide-h3kyjc7i7) · SitePoint · 0 upvotes · 0 comments - [Quantizing LLMs Step-by-Step: Converting FP16 Models to GGUF](https://daily.dev/posts/quantizing-llms-step-by-step-converting-fp16-models-to-gguf-lqamqetgk) · Machine Learning Mastery · 2 upvotes · 0 comments - [Quantization's Real Tradeoff: Where FP16, INT8, and GGUF Actually Diverge in Production by Model Size](https://daily.dev/posts/quantization-s-real-tradeoff-where-fp16-int8-and-gguf-actually-diverge-in-production-by-model-siz-gorxyymj9) · DigitalOcean Community · 0 upvotes · 0 comments - [Quantization Explained: Q4\_K\_M vs AWQ vs FP16 for Local LLMs](https://daily.dev/posts/quantization-explained-q4-k-m-vs-awq-vs-fp16-for-local-llms-8b024cuk3) · SitePoint · 1 upvotes · 0 comments --- Tags: [#ai-inference](https://daily.dev/tags/ai-inference), [#llama-cpp](https://daily.dev/tags/llama-cpp), [#machine-learning](https://daily.dev/tags/machine-learning), [#python](https://daily.dev/tags/python) [View this post on daily.dev](https://daily.dev/posts/quantization-explained-run-70b-models-on-consumer-gpus-2lmh0n2hc) ```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":"Quantization Explained: Run 70B Models on Consumer GPUs","url":"https://daily.dev/posts/quantization-explained-run-70b-models-on-consumer-gpus-2lmh0n2hc","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/quantization-explained-run-70b-models-on-consumer-gpus-2lmh0n2hc"},"datePublished":"2026-02-25T18:10:04.936Z","dateModified":"2026-05-14T02:12:56.282Z","description":"Running 70B parameter language models on consumer GPUs requires quantization to reduce memory footprint. 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