--- title: Full-Stack Optimizations for Agentic Inference with... description: NVIDIA Dynamo is being optimized for agentic inference workloads, addressing the write-once-read-many KV cache access patterns seen in tools like Claude Code... canonical: https://daily.dev/posts/full-stack-optimizations-for-agentic-inference-with-nvidia-dynamo-zsub45bhj twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Full-Stack Optimizations for Agentic Inference with NVIDIA Dynamo | daily.dev og:description: NVIDIA Dynamo is being optimized for agentic inference workloads, addressing the write-once-read-many KV cache access patterns seen in tools like Claude Code... og:url: https://daily.dev/posts/full-stack-optimizations-for-agentic-inference-with-nvidia-dynamo-zsub45bhj og:image: https://api.daily.dev/og/posts/ZSub45bhJ.png og:image:alt: Full-Stack Optimizations for Agentic Inference with NVIDIA Dynamo 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. # Full-Stack Optimizations for Agentic Inference with NVIDIA Dynamo **[NVIDIA Developer](https://daily.dev/sources/nvidiadev)** · 16 min read · 1 upvotes · 0 comments ## Summary NVIDIA Dynamo is being optimized for agentic inference workloads, addressing the write-once-read-many KV cache access patterns seen in tools like Claude Code and Codex. The post covers three layers of optimization: (1) a multi-protocol frontend supporting v1/responses, v1/messages, and v1/chat/completions with a new 'agent hints' API extension that lets harnesses pass scheduling signals like priority, output sequence length estimates, and speculative prefill hints; (2) a KV-aware router with a Flash Indexer achieving 170M ops/s, priority scheduling via a binary heap, and extensible Python-based custom routing strategies (NeMo Agent Toolkit achieved 4x p50 TTFT reduction); and (3) advanced KV cache management including a 4-tier memory hierarchy (GPU→CPU→disk→shared storage), selective retention via priority/TTL/token-range directives, cross-worker block sharing via NIXL/RDMA, and agent lifecycle awareness to mark ephemeral blocks (reasoning tokens, terminated subagent KV) for early eviction. The goal is to bring managed API-level cache reuse performance to self-hosted open-source model deployments. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [How to Reduce KV Cache Bottlenecks with NVIDIA Dynamo](https://daily.dev/posts/how-to-reduce-kv-cache-bottlenecks-with-nvidia-dynamo-30slrjanx) · NVIDIA Developer · 1 upvotes · 0 comments - [How NVIDIA Dynamo 1.0 Powers Multi-Node Inference at Production Scale](https://daily.dev/posts/how-nvidia-dynamo-1-0-powers-multi-node-inference-at-production-scale-b4qhmcdbs) · NVIDIA Developer · 0 upvotes · 0 comments - [Meet the New Standard for High-Performance, Low-Cost Inference: NVIDIA Dynamo 1.0 is now available to DigitalOcean Customers](https://daily.dev/posts/meet-the-new-standard-for-high-performance-low-cost-inference-nvidia-dynamo-1-0-is-now-available-t-fgqv2cjzg) · DigitalOcean · 1 upvotes · 0 comments - [Streaming Tokens and Tools: Multi-Turn Agentic Harness Support in NVIDIA Dynamo](https://daily.dev/posts/streaming-tokens-and-tools-multi-turn-agentic-harness-support-in-nvidia-dynamo-5rhbl5ps2) · NVIDIA Developer · 1 upvotes · 0 comments - [NVIDIA Dynamo Addresses Multi-Node LLM Inference Challenges](https://daily.dev/posts/nvidia-dynamo-addresses-multi-node-llm-inference-challenges-yz7e3pwz7) · InfoQ · 0 upvotes · 0 comments --- Tags: [#ai-agents](https://daily.dev/tags/ai-agents) [View this post on daily.dev](https://daily.dev/posts/full-stack-optimizations-for-agentic-inference-with-nvidia-dynamo-zsub45bhj) ```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":"Full-Stack Optimizations for Agentic Inference with NVIDIA Dynamo","url":"https://daily.dev/posts/full-stack-optimizations-for-agentic-inference-with-nvidia-dynamo-zsub45bhj","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/full-stack-optimizations-for-agentic-inference-with-nvidia-dynamo-zsub45bhj"},"datePublished":"2026-04-17T22:54:22.055Z","dateModified":"2026-04-17T22:54:49.841Z","description":"NVIDIA Dynamo is being optimized for agentic inference workloads, addressing the write-once-read-many KV cache access patterns seen in tools like Claude Code...","image":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/cbc40bb9daee97177cceef0a4a3092a6?_a=AQAEuop","thumbnailUrl":"https://media.daily.dev/image/upload/f_auto,q_auto/v1/posts/cbc40bb9daee97177cceef0a4a3092a6?_a=AQAEuop","isAccessibleForFree":true,"articleSection":"NVIDIA 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":"NVIDIA Developer","logo":"https://media.daily.dev/image/upload/t_logo,f_auto/v1/logos/86e45aab42ba48ce83103d01b1119910","url":"https://daily.dev/sources/nvidiadev"},"commentCount":0,"discussionUrl":"https://daily.dev/posts/full-stack-optimizations-for-agentic-inference-with-nvidia-dynamo-zsub45bhj","interactionStatistic":[{"@type":"InteractionCounter","interactionType":{"@type":"LikeAction"},"userInteractionCount":1},{"@type":"InteractionCounter","interactionType":{"@type":"CommentAction"},"userInteractionCount":0}],"keywords":"ai-agents","timeRequired":"PT16M"} {"@context":"https://schema.org","@type":"BreadcrumbList","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https://daily.dev"},{"@type":"ListItem","position":2,"name":"NVIDIA Developer","item":"https://daily.dev/sources/nvidiadev"},{"@type":"ListItem","position":3,"name":"Full-Stack Optimizations for Agentic Inference with NVIDIA Dynamo"}]} ```