--- title: Optimizing Self-Hosted Gemma for Production Inference description: A detailed production guide for serving Gemma 4 31B on vLLM using two NVIDIA RTX PRO 6000 Blackwell GPUs. Covers memory-oriented optimizations like setting... canonical: https://daily.dev/posts/optimizing-self-hosted-gemma-for-production-inference-tluaahoqb twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Optimizing Self-Hosted Gemma for Production Inference | daily.dev og:description: A detailed production guide for serving Gemma 4 31B on vLLM using two NVIDIA RTX PRO 6000 Blackwell GPUs. Covers memory-oriented optimizations like setting... og:url: https://daily.dev/posts/optimizing-self-hosted-gemma-for-production-inference-tluaahoqb og:image: https://api.daily.dev/og/posts/tluAAHoQb.png og:image:alt: Optimizing Self-Hosted Gemma for Production 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. # Optimizing Self-Hosted Gemma for Production Inference **[Callstack Blog](https://daily.dev/sources/callstack-blog)** · 9 min read · 0 upvotes · 0 comments ## Summary A detailed production guide for serving Gemma 4 31B on vLLM using two NVIDIA RTX PRO 6000 Blackwell GPUs. Covers memory-oriented optimizations like setting --max-model-len and disabling unused multimodal inputs, then shows how enabling Gemma 4 Multi-Token Prediction (speculative decoding) via the assistant checkpoint achieves 2.48x output-token throughput at concurrency 1 and 1.92x at concurrency 10, with diminishing returns at concurrency 100. Also covers 8-GPU scaling with four TP2 replicas, an SGLang alternative, and a production readiness checklist. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [I tested Google's new Gemma 4 12B on my 8GB GPU, and now I don't want to go back to smaller models](https://daily.dev/posts/i-tested-google-s-new-gemma-4-12b-on-my-8gb-gpu-and-now-i-don-t-want-to-go-back-to-smaller-models-7rdzvdak0) · XDA Developers · 0 upvotes · 0 comments - [Google’s Gemma 4 shines on local systems – both big and small](https://daily.dev/posts/google-s-gemma-4-shines-on-local-systems-both-big-and-small-qpert58dk) · InfoWorld · 0 upvotes · 0 comments --- Tags: [#ai-inference](https://daily.dev/tags/ai-inference), [#vllm](https://daily.dev/tags/vllm), [#gemma](https://daily.dev/tags/gemma) [View this post on daily.dev](https://daily.dev/posts/optimizing-self-hosted-gemma-for-production-inference-tluaahoqb) ```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 Self-Hosted Gemma for Production Inference","url":"https://daily.dev/posts/optimizing-self-hosted-gemma-for-production-inference-tluaahoqb","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/optimizing-self-hosted-gemma-for-production-inference-tluaahoqb"},"datePublished":"2026-06-05T11:48:22.542Z","dateModified":"2026-06-05T11:48:47.689Z","description":"A detailed production guide for serving Gemma 4 31B on vLLM using two NVIDIA RTX PRO 6000 Blackwell GPUs. Covers memory-oriented optimizations like setting...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/ebd0df2182eb9c1f05b1e8f294e668dc?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/ebd0df2182eb9c1f05b1e8f294e668dc?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"Callstack Blog","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":"Callstack Blog","logo":"https://media.daily.dev/image/upload/s--3qItoroi--/f_auto,q_auto/v1774179048/logos/callstack-blog?_a=BAMAMiWQ0","url":"https://daily.dev/sources/callstack-blog"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/optimizing-self-hosted-gemma-for-production-inference-tluaahoqb","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":0},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"ai-inference,vllm,gemma","timeRequired":"PT9M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Callstack Blog","item":"https://daily.dev/sources/callstack-blog"},{"@type":"ListItem","position":3,"name":"Optimizing Self-Hosted Gemma for Production Inference"}]} ```