Speculative decoding tutorial eyes persistent inference sessions

The real insight here isn't "what is speculative decoding" — it's that the technique exposes a structural mismatch between how LLMs are deployed and how they run most efficiently.

• –Speculative decoding uses a lightweight draft model to predict K tokens ahead, which the large target model verifies in a single forward pass — yielding 2–3x latency gains with zero quality tradeoff
• –The gains are maximized when KV cache is already warm; stateless APIs that cold-start each request undercut this by design
• –The industry is converging on KV-cache-aware routing (e.g., llm-d, LMCache) that makes sessions "sticky" at the infra layer — solving the problem without exposing persistent session primitives to API consumers
• –Variants like EAGLE-3 and SuffixDecoding push further: EAGLE-3 attaches a prediction head to the target model's own layers, while SuffixDecoding builds suffix trees from prior outputs for model-free speculation at 5x+ speedup on agentic tasks
• –This is a meaningful question for developers building multi-turn agents on top of inference APIs — stateless APIs are paying a real performance tax