SGLang RadixAttention: 6.4x Inference Throughput via Shared KV Cache Prefix Reuse for Agentic Workloads

Markaicode·medium signal

SGLang's RadixAttention stores the KV cache of both prompts and generated tokens in a radix tree, enabling automatic cache reuse whenever multiple requests share a common prefix (e.g., the same system prompt, few-shot examples, or tool definitions). For agentic pipelines where every subagent call shares a long system context, this eliminates redundant computation across requests. Benchmarks show 6.4x higher throughput vs. baseline inference; February 2026 results show 25x improvement on NVIDIA GB300 NVL72. Drop-in replacement for vLLM via the OpenAI-compatible server.