Why SGLang is Faster than vLLM for Structured Generation

In the race for LLM performance, SGLang (Structured Generation Language) is pushing the boundaries even further than vLLM, especially for tasks that require specific, structured outputs like JSON.

RadixAttention: The Secret Sauce

While vLLM uses PagedAttention, SGLang introduces RadixAttention. This technology allows the server to "cache" and reuse the prefix of a prompt across many different requests. For systems with large system prompts or complex few-shot examples, this means the AI doesn't have to re-process the same information every time, resulting in massive speedups.

Optimized Parallel Decoding

SGLang is designed to handle "structured" programs where multiple AI calls might happen in parallel. Its runtime is optimized to manage these complex dependencies, ensuring that even the most sophisticated multi-step AI logic runs with the lowest possible latency and maximum GPU utilization.