--- title: LLM quantization guide: How to do it, and how it helps description: A practical guide to LLM quantization explains what it does to a model, where in the inference stack it pays off, and how to choose between algorithms and... canonical: https://daily.dev/posts/llm-quantization-guide-how-to-do-it-and-how-it-helps-ibtxb1uok twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: LLM quantization guide: How to do it, and how it helps | daily.dev og:description: A practical guide to LLM quantization explains what it does to a model, where in the inference stack it pays off, and how to choose between algorithms and... og:url: https://daily.dev/posts/llm-quantization-guide-how-to-do-it-and-how-it-helps-ibtxb1uok og:image: https://api.daily.dev/og/posts/ibTXB1uok.png og:image:alt: LLM quantization guide: How to do it, and how it helps 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. # LLM quantization guide: How to do it, and how it helps **[Red Hat Developer](https://daily.dev/sources/rhdev)** · 7 min read · 0 upvotes · 0 comments ## Summary A practical guide to LLM quantization explains what it does to a model, where in the inference stack it pays off, and how to choose between algorithms and formats like GGUF, AWQ, GPTQ, FP8, and NVFP4. It breaks down the difference between weight and activation quantization, memory savings (e.g., Llama 4 Scout going from ~220GB at BF16 to ~55GB at INT4/FP4), and accuracy recovery data from Red Hat's NVFP4 releases. Benchmarks on Llama 3 70B show throughput jumping from 158 to 474 tokens/sec and time-to-first-token dropping from over 30,000ms to about 4,800ms when moving from FP16 to FP8. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### How much GPU memory does quantizing Llama 4 Scout from BF16 to INT4 actually save? Llama 4 Scout at 109B parameters requires about 220GB at BF16 (three 80GB GPUs), about 109GB at INT8/FP8 (two GPUs), and about 55GB at INT4/FP4 (one GPU). That is a 75% reduction in memory footprint going from BF16 to INT4, letting the same model run on a single GPU instead of three. _Teams sizing GPU clusters for large models can follow quantization comparisons like this on daily.dev._ ### What is the difference between weight quantization and activation quantization in LLM inference? Weight quantization reduces the precision of a model's learned parameters, cutting data movement between HBM and on-chip SRAM, which speeds up the memory-bound decoding phase of inference. Activation quantization lowers the precision of tensors flowing through the model, letting tensor cores process more operations per second, which speeds up the compute-bound prefill phase. Quantizing both together, denoted W{bits}A{bits}, unlocks the full speedup. _Engineers tuning inference throughput can track quantization schemes like W8A8 on daily.dev._ ### How much does FP8 quantization improve LLM inference throughput compared to FP16? Benchmarking Llama 3 70B on two H100 GPUs with a RAG-style workload of roughly 1024 input and 128 output tokens, throughput rose from 158 to 474 input tokens per second and time to first token under load dropped from over 30,000ms to about 4,800ms when switching from FP16 to FP8 weights and activations, using the same GPUs and same model. _Developers benchmarking inference precision trade-offs can follow results like these on daily.dev._ ## Similar posts on daily.dev - [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 - [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 - [5 LLM Quantization Techniques](https://daily.dev/posts/5-llm-quantization-techniques-nfunrpnuc) · Daily Dose of Data Science \| Avi Chawla \| Substack · 3 upvotes · 1 comments - [Untitled](https://daily.dev/posts/untitled-qawtuhuih) · SitePoint · 1 upvotes · 0 comments --- Tags: [#data-science](https://daily.dev/tags/data-science), [#gpu](https://daily.dev/tags/gpu), [#ai-inference](https://daily.dev/tags/ai-inference), [#vllm](https://daily.dev/tags/vllm) [View this post on daily.dev](https://daily.dev/posts/llm-quantization-guide-how-to-do-it-and-how-it-helps-ibtxb1uok) ```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":"LLM quantization guide: How to do it, and how it helps","url":"https://daily.dev/posts/llm-quantization-guide-how-to-do-it-and-how-it-helps-ibtxb1uok","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/llm-quantization-guide-how-to-do-it-and-how-it-helps-ibtxb1uok"},"datePublished":"2026-09-02T03:43:09.696Z","dateModified":"2026-09-02T03:43:32.537Z","description":"A practical guide to LLM quantization explains what it does to a model, where in the inference stack it pays off, and how to choose between algorithms and...","image":"https://media.daily.dev/image/upload/s--OHB84bZF--/f_auto/v1722860399/public/Placeholder%2010","thumbnailUrl":"https://media.daily.dev/image/upload/s--OHB84bZF--/f_auto/v1722860399/public/Placeholder%2010","isAccessibleForFree":true,"articleSection":"Red Hat Developer","inLanguage":"en","publisher":{"@type":"Organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180}},"author":{"@type":"Organization","name":"Red Hat Developer","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/2a8895f3216c4900bdc455585a4ad3e4","url":"https://daily.dev/sources/rhdev"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/llm-quantization-guide-how-to-do-it-and-how-it-helps-ibtxb1uok","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":0},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"data-science,gpu,ai-inference,vllm","timeRequired":"PT7M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Red Hat Developer","item":"https://daily.dev/sources/rhdev"},{"@type":"ListItem","position":3,"name":"LLM quantization guide: How to do it, and how it helps"}]} {"@context":"https://schema.org","@type":"FAQPage","@id":"https://daily.dev/posts/llm-quantization-guide-how-to-do-it-and-how-it-helps-ibtxb1uok#faq","mainEntity":[{"@type":"Question","name":"How much GPU memory does quantizing Llama 4 Scout from BF16 to INT4 actually save?","acceptedAnswer":{"@type":"Answer","text":"Llama 4 Scout at 109B parameters requires about 220GB at BF16 (three 80GB GPUs), about 109GB at INT8/FP8 (two GPUs), and about 55GB at INT4/FP4 (one GPU). 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