We are building a RAG-based agent that outputs strictly formatted JSON. We noticed vLLM's structured decoding can be slow. Can anyone confirm if SGLang faster than vLLM for agent workflows that require high-speed constrained generation?
3 answers
SGLang absolutely shines here. It integrates a compressed finite state machine (FSM) approach for constrained decoding that is significantly more optimized than the standard Outlines integration used in vLLM. In our tests, generating complex JSON schemas was nearly 3x faster on SGLang. This is crucial for agents because every extra second of latency in a 10-step reasoning chain adds up. Since agents often repeat the same schema, SGLang's ability to cache that "structure" and the preceding prompt makes it the superior choice for high-volume, structured agent tasks where precision and speed are both required.
Deborah, have you tried this with the new DeepSeek models? I'm curious if the MoE architecture changes the performance gap between these two engines when handling JSON?
Yes, SGLang is faster for JSON. It uses specialized kernels that make constrained decoding much more efficient than the generic methods.
I can confirm this. Our agent latency dropped by 40% just by switching the backend to SGLang for our JSON-heavy extraction pipelines.
Joshua, SGLang actually has specific optimizations for DeepSeek’s Mixture-of-Experts. It handles the dynamic routing of experts better than vLLM's current implementation, which makes it even more effective when you combine MoE models with structured output.