--- title: Configuring Dedicated Model Inference | daily.dev description: Together AI's Dedicated Model Inference platform uses three core resources: configs (immutable recipes specifying GPU type, parallelism, and optimization... canonical: https://daily.dev/posts/configuring-dedicated-model-inference-ux6jic7fr twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Configuring Dedicated Model Inference | daily.dev og:description: Together AI's Dedicated Model Inference platform uses three core resources: configs (immutable recipes specifying GPU type, parallelism, and optimization... og:url: https://daily.dev/posts/configuring-dedicated-model-inference-ux6jic7fr og:image: https://api.daily.dev/og/posts/UX6jic7fr.png og:image:alt: Configuring Dedicated Model 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. # Configuring Dedicated Model Inference **[Together AI](https://daily.dev/sources/togetherai)** · 8 min read · 0 upvotes · 0 comments ## Summary Together AI's Dedicated Model Inference platform uses three core resources: configs (immutable recipes specifying GPU type, parallelism, and optimization profile), deployments (model + config bindings with autoscaling), and endpoints (stable names with capacity-aware traffic splits). Traffic routing is weight-based per ready replica, meaning effective capacity equals weight × ready_replicas, so autoscaling and routing compose naturally. The post explains how A/B tests, shadow experiments, canary rollouts, and zero-downtime swaps all reduce to manipulating deployments and traffic weights. A live benchmark with two H100 deployments demonstrates how replica scaling shifts traffic proportionally, and a concurrency sweep comparing latency vs. throughput profiles shows that the throughput profile saturates past concurrency 4 while the latency profile scales linearly to c16. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [A/B test models in production](https://daily.dev/posts/a-b-test-models-in-production-w0dbvgkzs) · Together AI · 0 upvotes · 0 comments - [Autoscaling endpoints for LLM inference](https://daily.dev/posts/autoscaling-endpoints-for-llm-inference-ddy21ncoc) · Together AI · 0 upvotes · 0 comments - [The production platform for open-weight AI inference](https://daily.dev/posts/the-production-platform-for-open-weight-ai-inference-rcdyvkjtr) · Together AI · 0 upvotes · 0 comments - [Three Generations of Autoscaling — And Why Agentic Traffic Breaks All of Them](https://daily.dev/posts/three-generations-of-autoscaling-and-why-agentic-traffic-breaks-all-of-them-e5fyauhll) · Towards Data Science · 1 upvotes · 0 comments - [Training to Inference: Why AI Cloud Must Catch Up](https://daily.dev/posts/training-to-inference-why-ai-cloud-must-catch-up-bf9dfa1dg) · DigitalOcean Community · 0 upvotes · 0 comments --- [View this post on daily.dev](https://daily.dev/posts/configuring-dedicated-model-inference-ux6jic7fr) ```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":"Configuring Dedicated Model Inference","url":"https://daily.dev/posts/configuring-dedicated-model-inference-ux6jic7fr","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/configuring-dedicated-model-inference-ux6jic7fr"},"datePublished":"2026-07-28T04:27:48.044Z","dateModified":"2026-07-28T04:28:10.478Z","description":"Together AI's Dedicated Model Inference platform uses three core resources: configs (immutable recipes specifying GPU type, parallelism, and optimization...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/c04dd98067dd54e3e6ed39288b2b2594?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/c04dd98067dd54e3e6ed39288b2b2594?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"Together AI","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":"Together AI","logo":"https://media.daily.dev/image/upload/s--tCjWcJfJ--/f_auto,q_auto/v1780213200/logos/togetherai?_a=BAMAMiWQ0","url":"https://daily.dev/sources/togetherai"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/configuring-dedicated-model-inference-ux6jic7fr","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":0},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"","timeRequired":"PT8M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"Together AI","item":"https://daily.dev/sources/togetherai"},{"@type":"ListItem","position":3,"name":"Configuring Dedicated Model Inference"}]} ```