SGLang

SGLang is an open-source, high-performance serving framework for large language models and multimodal models. Originating from research associated with UC Berkeley and the LMSYS group, the name stands for “Structured Generation Language,” reflecting its origins as a system for programming complex, multi-step LLM interactions. It is one of the serving engines a self-hoster can run to expose a model over an OpenAI-compatible API on owned hardware.

RadixAttention and prefix reuse

SGLang's distinguishing feature is RadixAttention, which organizes the key-value cache as a radix tree indexed by token sequences. When many requests share a common prefix, such as the same system prompt or a shared conversation history, the framework detects and reuses the already-computed cache instead of recomputing it. For prefix-heavy workloads this can substantially raise throughput and cut latency, which is why it is used in large production deployments serving very high token volumes.

Structured output

SGLang also focuses on constrained or structured generation. It uses compressed finite-state-machine techniques to accelerate decoding when output must conform to a schema, such as JSON, making schema-bound responses both faster and more reliable. This is useful when an LLM feeds a downstream program that expects a fixed shape rather than free-form prose.

SGLang competes in the same space as other GPU serving engines; see vLLM for a widely used alternative and model quantization for reducing the memory a served model requires.