I've been experimenting with speculative decoding to reduce latency. Is SGLang faster than vLLM for agent workflows when using a draft model to speed up generation? Which engine has better support for this?
3 answers
SGLang's implementation of speculative decoding is incredibly robust. Because it manages the KV cache via a radix tree, it can handle the multiple "speculative" branches more efficiently than the linear approach in many other engines. For agents, where the next token is often predictable (like in tool-calling syntax), speculative decoding can provide another 2x boost in speed. While vLLM supports it, we've found SGLang's integration to be slightly more stable and easier to configure for custom draft models. If you combine speculative decoding with prefix caching, SGLang becomes an absolute powerhouse for low-latency agent interactions.
Pamela, does using a draft model significantly increase the VRAM overhead for local setups? I'm worried about running out of memory on a single 3090/4090?
Both support it, but SGLang's architecture handles the "tree" of tokens generated by speculation much more naturally, leading to better real-world speedups.
It's all about that Radix backend. It handles the branching and verification logic of speculative decoding like a pro.
Patrick, a small draft model like a 1.5B or 0.5B model takes very little extra VRAM. The speed gains usually justify the small memory cost. On a 24GB card, you should have plenty of room to run a 7B or 8B model alongside a tiny draft model without any issues.