--- title: How to Choose the Right GPU for vLLM Inference | daily.dev description: vLLM inference performance depends on understanding prefill (memory-bandwidth bound) and decode (compute-bound) phases. GPU selection requires calculating VRAM... canonical: https://daily.dev/posts/how-to-choose-the-right-gpu-for-vllm-inference-1felswdq3 twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: How to Choose the Right GPU for vLLM Inference | daily.dev og:description: vLLM inference performance depends on understanding prefill (memory-bandwidth bound) and decode (compute-bound) phases. GPU selection requires calculating VRAM... og:url: https://daily.dev/posts/how-to-choose-the-right-gpu-for-vllm-inference-1felswdq3 og:image: https://api.daily.dev/og/posts/1fElsWdQ3.png og:image:alt: How to Choose the Right GPU for vLLM Inference 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. # How to Choose the Right GPU for vLLM Inference **[DigitalOcean Community](https://daily.dev/sources/do_community)** · 16 min read · 1 upvotes · 0 comments ## Summary vLLM inference performance depends on understanding prefill (memory-bandwidth bound) and decode (compute-bound) phases. GPU selection requires calculating VRAM needs based on model weights (parameters × bytes per parameter) plus KV cache (grows with context length and concurrency). Quantization (FP16→INT4) reduces memory by 75%, enabling large models on smaller GPUs. FP8 offers optimal speed-quality balance on modern hardware. Tensor Parallelism pools VRAM across GPUs but adds communication overhead. A 70B FP16 model needs 140GB for weights alone; INT4 quantization reduces this to 35GB. KV cache for 70B with 32k context and 10 users requires ~112GB (FP16) or 56GB (FP8). Always reserve 4-8GB VRAM overhead for system operations. ## 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 - [VRAM for 70B Models: Why 16GB GPU Is the Minimum in 2026](https://daily.dev/posts/vram-for-70b-models-why-16gb-gpu-is-the-minimum-in-2026-ymxwh5kfa) · SitePoint · 1 upvotes · 0 comments - [Where does all the VRAM go during LLM inference?](https://daily.dev/posts/where-does-all-the-vram-go-during-llm-inference--n5qbtkkvj) · Daily Dose of Data Science \| Avi Chawla \| Substack · 1 upvotes · 0 comments - [The KV Cache Tax: Why Inference Servers Run Out of Memory Before Compute](https://daily.dev/posts/the-kv-cache-tax-why-inference-servers-run-out-of-memory-before-compute-yzoonznpj) · Towards Data Science · 0 upvotes · 0 comments - [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 --- Tags: [#gpu](https://daily.dev/tags/gpu), [#llm](https://daily.dev/tags/llm), [#machine-learning](https://daily.dev/tags/machine-learning), [#nvidia](https://daily.dev/tags/nvidia), [#vllm](https://daily.dev/tags/vllm) [View this post on daily.dev](https://daily.dev/posts/how-to-choose-the-right-gpu-for-vllm-inference-1felswdq3) ```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":"How to Choose the Right GPU for vLLM Inference","url":"https://daily.dev/posts/how-to-choose-the-right-gpu-for-vllm-inference-1felswdq3","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/how-to-choose-the-right-gpu-for-vllm-inference-1felswdq3"},"datePublished":"2026-01-12T18:19:17.022Z","dateModified":"2026-10-15T02:21:09.826Z","description":"vLLM inference performance depends on understanding prefill (memory-bandwidth bound) and decode (compute-bound) phases. 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