Which engine has lower TTFT for multi-turn conversations?

All answers to this question.

SGLang secures a definitive victory regarding Time to First Token in extended multi-turn dialogs due to its RadixAttention layout. In a standard vLLM deployment, every time a user sends a new message, the engine must recompute the Key-Value states for the entire historical transcript from scratch unless explicit, manual prefix-caching parameters are carefully configured. SGLang treats the entire memory pool as an evolving cache graph. It matches the incoming message's historical prefix against the active radix tree and immediately retrieves the processed KV states, dropping the TTFT from seconds down to milliseconds.

   Answered 2025-12-06 by Victoria Vance

How does this cache mechanism behave when dealing with highly parallel requests that branch off from a single master conversation thread? If we are running tree-of-thought reasoning where one prompt spawns ten different parallel historical continuations, does SGLang maintain its performance edge?

   Answered 2025-12-09 by Arthur Dent

• Arthur Dent That scenario is exactly where SGLang shines brightest. Because its memory manager is literally organized as a radix tree structure, branching paths share the exact same root memory node. The engine processes the shared root once, and all ten parallel forks pull from that single cache block without duplicating VRAM usage.

     Commented 2025-12-11 by George Clooney

SGLang delivers a significantly lower TTFT because it treats conversation history as a persistent cache asset rather than transient text.

   Answered 2025-12-15 by Harvey Dent

• Harvey Dent Exactly. For deep, 20-turn conversations, avoiding the full re-computation of the context window on every turn saves incredible amounts of GPU processing time and keeps the user experience snappy.

     Commented 2025-12-18 by Selina Kyle