5 SGLang RadixAttention configs that cut agent inference latency by half
Last Updated on August 25, 2026 by Editorial Team
Author(s): allglenn
Originally published on Towards AI.
Five practical configurations for faster prefix reuse, lower time-to-first-token, and more responsive agent workloads.
Your agent sends a 2,000-token system prompt on every request. Your inference server recomputes the KV activations for those 2,000 tokens from scratch every time. At 50 concurrent sessions, that is roughly 100,000 tokens of pure redundant work per batch. Your GPU is paying for all of it in latency.
The article explains how SGLang’s RadixAttention can cut agent inference latency by reusing KV cache across generation calls, and why many teams see little benefit when features are “enabled” but not tuned. After introducing RadixAttention’s radix-tree KV caching and the importance of stable prefix matching, it lays out five production-oriented configurations: (1) pin the system prompt and tool definitions to an exact, byte-identical prefix to prevent cache misses from drift; (2) use prefill–decode disaggregation so long-context prefill doesn’t block decoding, including chunked prefill and (when justified) full disaggregation; (3) select and tune the radix tree eviction policy and memory fraction based on session patterns; (4) co-design structured output with RadixAttention by keeping schemas stable so XGrammar grammar caches compound with prefix caching; and (5) monitor cache hit rate in production via metrics, using alerting and ratio checks to verify that RadixAttention is truly firing. It also compares RadixAttention against alternatives like vLLM’s automatic prefix caching, TensorRT-LLM, and HuggingFace TGI, and gives guidance on choosing engines based on workload shape, along with a practical checklist to deploy and validate improvements.
Read the full blog for free on Medium.
Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.
Published via Towards AI
Towards AI Academy
We Build Enterprise-Grade AI. We'll Teach You to Master It Too.
15 engineers. 100,000+ students. Towards AI Academy teaches what actually survives production.
Start free — no commitment:
→ 6-Day Agentic AI Engineering Email Guide — one practical lesson per day
→ Agents Architecture Cheatsheet — 3 years of architecture decisions in 6 pages
Our courses:
→ AI Engineering Certification — 90+ lessons from project selection to deployed product. The most comprehensive practical LLM course out there.
→ Agent Engineering Course — Hands on with production agent architectures, memory, routing, and eval frameworks — built from real enterprise engagements.
→ AI for Work — Understand, evaluate, and apply AI for complex work tasks.
Note: Article content contains the views of the contributing authors and not Towards AI.
Related posts
Recent Posts
