EAGLE-3 Speculative Decoding in Production

Speculative decoding pairs a fast draft model with the target model. The draft proposes K tokens; the target verifies in a single forward; accepted tokens are free. In 2026, EAGLE-3 is the production-grade variant — it trains a draft head on the target model's hidden states rather than on raw tokens, pushing acceptance rate alpha into the 0.6-0.8 band on general chat. The right question is not "how fast is the draft" but "what is alpha on my traffic?" If alpha drops below ~0.55, speculative decoding is net negative at high concurrency because every rejected draft costs a second target forward pass. This lesson teaches you to measure alpha first and flip the flag second.

Learn 1 hr Python (stdlib, toy acceptance-rate simulator)

Prerequisites: Phase 17 · 04 (vLLM Serving Internals), Phase 10 · 18 (Multi-Token Prediction)

#Learning Objectives

Name the three generations of speculative decoding and explain what EAGLE-3 changes from EAGLE-2 and from a classic draft model.
Define acceptance rate alpha, compute expected speedup from alpha and K (draft length), and identify the break-even alpha for your target concurrency.
Explain why speculative decoding is opt-in (not default) in vLLM 2026 and why turning it on without measuring alpha is a production anti-pattern.
Write a measurement plan: which benchmark, which prompt distribution, which concurrency point, which metric to gate on.

#Learning Objectives

Keep going. The rest of this lesson is one step away.