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 down to 92ms, a 5.21x improvement (Qwen2.5-Coder-32B-Instruct-AWQ), as benchmarked by NextByteTech in their Dev.to post on ollama vs vLLM vs SGLang in 2026.

The data is real, and the conclusion is mostly right: for long-context, multi-turn agent traffic, SGLang currently wins. But "mostly" hides the interesting part. Those speedups only materialize when your workload has the right shape. Run the same two engines against different traffic and the gap collapses — or flips.…