We are deploying a local cluster for our internal AI assistants. We need reliability and speed. Is SGLang faster than vLLM for agent workflows in a production environment with multiple users?
3 answers
For production, the answer depends on your hardware. If you are on NVIDIA H100s, SGLang is currently seeing a 20-30% throughput advantage because it's been hyper-optimized for Hopper architecture. For agents, the stability of the TTFT is the most important metric. SGLang provides a more consistent "snappy" feeling because it hits the cache so often. However, vLLM has a more mature ecosystem with better Kubernetes support and integrations like Ray. If you need raw, bleeding-edge speed for agent loops today, go SGLang. If you need a battle-tested framework with the best documentation, vLLM is still the safer bet.
Heather, do you think the gap will close once vLLM fully implements its own version of automatic prefix caching? I've seen some PRs moving in that direction recently?
SGLang is the speed king for agents right now. vLLM is the "industry standard" for general-purpose batching and ease of use.
That sums it up perfectly. Use SGLang if you're pushing the limits of what agents can do, otherwise stick with vLLM for simplicity.
Scott, vLLM is definitely catching up, but SGLang's architecture was built from the ground up for this "Language Model Program" style of interaction. It's not just the cache; it's the entire runtime execution model. SGLang will likely keep the lead for complex agentic logic for a while.