--- title: Why Small Models Alone Don't Reduce Inference Costs description: Using small language models (SLMs) instead of large frontier models doesn't automatically reduce inference costs — the real savings only appear when multiple... canonical: https://daily.dev/posts/why-small-models-alone-don-t-reduce-inference-costs-475vvadkn twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Why Small Models Alone Don't Reduce Inference Costs | daily.dev og:description: Using small language models (SLMs) instead of large frontier models doesn't automatically reduce inference costs — the real savings only appear when multiple... og:url: https://daily.dev/posts/why-small-models-alone-don-t-reduce-inference-costs-475vvadkn og:image: https://api.daily.dev/og/posts/475VVAdKN.png og:image:alt: Why Small Models Alone Don't Reduce Inference Costs 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. # Why Small Models Alone Don't Reduce Inference Costs **[Daily Dose of Data Science \| Avi Chawla \| Substack](https://daily.dev/sources/dailydoseofds)** · 18 min read · 0 upvotes · 0 comments ## Summary Using small language models (SLMs) instead of large frontier models doesn't automatically reduce inference costs — the real savings only appear when multiple models share the same GPU. Standard serving tools like vLLM and TEI are each designed for one model per server, forcing one GPU per model and recreating the idle-hardware waste teams were trying to escape. The post explains why multi-model GPU sharing is hard with existing tooling, then introduces the open-source Superlinked Inference Engine (SIE) as a solution. SIE runs embeddings, reranking, extraction, OCR, and generation through a single API and server, uses LRU-based model loading/eviction, batches by compute cost to minimize padding waste, and ships with autoscaling, routing, and Terraform for AWS/GCP. A complete code walkthrough demonstrates a four-model retrieval pipeline (encode → score → extract → generate) all hitting one endpoint. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [\[Hands-on\] How to Serve 5 Models On One GPU](https://daily.dev/posts/hands-on-how-to-serve-5-models-on-one-gpu-aw1zj8taj) · Daily Dose of Data Science \| Avi Chawla \| Substack · 0 upvotes · 0 comments - [Serverless vs. On-prem vs. Edge Deployment](https://daily.dev/posts/serverless-vs-on-prem-vs-edge-deployment-kebs8beeq) · Daily Dose of Data Science \| Avi Chawla \| Substack · 0 upvotes · 0 comments - [Disaggregation in Large Language Models: The Next Evolution in AI Infrastructure](https://daily.dev/posts/disaggregation-in-large-language-models-the-next-evolution-in-ai-infrastructure-i3bji5n2f) · InfoQ · 0 upvotes · 0 comments --- [View this post on daily.dev](https://daily.dev/posts/why-small-models-alone-don-t-reduce-inference-costs-475vvadkn) ```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":"Why Small Models Alone Don't Reduce Inference Costs","url":"https://daily.dev/posts/why-small-models-alone-don-t-reduce-inference-costs-475vvadkn","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/why-small-models-alone-don-t-reduce-inference-costs-475vvadkn"},"datePublished":"2026-07-15T20:45:56.401Z","dateModified":"2026-07-15T20:46:21.108Z","description":"Using small language models (SLMs) instead of large frontier models doesn't automatically reduce inference costs — the real savings only appear when multiple...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/6e6047e17d2e037da7adafb7d3a2669a?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/6e6047e17d2e037da7adafb7d3a2669a?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"Daily Dose of Data Science | Avi Chawla | Substack","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":"Daily Dose of Data Science | Avi Chawla | Substack","logo":"https://media.daily.dev/image/upload/s--4IHQgTOw--/f_auto/v1710503712/logos/dailydoseofds","url":"https://daily.dev/sources/dailydoseofds"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/why-small-models-alone-don-t-reduce-inference-costs-475vvadkn","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":0},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"","timeRequired":"PT18M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Daily Dose of Data Science | Avi Chawla | Substack","item":"https://daily.dev/sources/dailydoseofds"},{"@type":"ListItem","position":3,"name":"Why Small Models Alone Don't Reduce Inference Costs"}]} ```