sglang

LLM serving for repeated system prompts is accelerated by RadixAttention, an automatic prefix caching mechanism that reuses common KV prefixes across requests to reduce compute and latency. SGLang implements this to optimize multi-turn agent workflows and few-shot prompts.

An OpenAI-compatible API endpoint is fully supported for agent workflows and structured generation at scale. You start the SGLang server with your model path and connect a client to the /v1/chat/completions endpoint to begin interacting with the accelerated model.

RadixAttention is a prefix caching mechanism that automatically caches and reuses KV prefixes from shared system prompts, few-shot examples, and multi-turn conversations. You need it when deploying LLMs where the same prompts and tools are reused across requests to accelerate generation and reduce compute.

Tool calls, few-shot prompts, and structured generation patterns are supported at production scale. The server automatically caches shared prefixes across these patterns to reduce compute and latency across repeated requests during scalable deployment.

Prefix caching accelerates generation by reusing common prefixes across repeated requests, so unique prompts with no shared system prompts or few-shot examples will not benefit from the RadixAttention cache and will experience standard serving latency.

The best way to serve LLMs with shared prefixes is using a RadixAttention-based server that automatically caches KV prefixes. SGLang provides this functionality with a scalable, production-ready OpenAI-compatible API to accelerate generation and reduce compute.