SGLang vs vLLM: Your Agent Is Slow Because You Picked the Wrong Cache
This story is from 2026-10-23. It is preserved in the archive; the latest stories are on the live feed.
If you serve LLM agents in production, you have probably seen this week's numbers: in a multi-turn agent workload with an 8,000-token system context, SGLang's RadixAttention cut time-to-first-token (TTFT) from 440ms to 38ms — an 11.57x speedup — while vLLM v0.6 with prefix caching managed 480ms dow…
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- 2026-10-23 01:58 · DEV Community — Machine Learning
SGLang vs vLLM: Your Agent Is Slow Because You Picked the Wrong Cache