Study notes: how speculative decoding accelerates LLM inference without quality loss
helloiamleonie · x · 2026-08-24
Leonie Monigatti published study notes on speculative decoding, the inference optimization technique from DeepMind and Google (2023).
- Why: autoregressive generation makes latency proportional to output length, a bottleneck for real-time chat and agentic workflows.
- How: a cheap draft model proposes γ candidate tokens, verified in parallel by the target model in one forward pass; rejection sampling preserves the target distribution, so speedups come with no quality loss.
- Covers draft mechanism variants including Medusa, EAGLE, DFlash, and DSpark.
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