Part 6
Completed
Speculative Decoding
Implemented speculative decoding to accelerate autoregressive generation by 2-3x without quality loss. Used a small draft model to predict future tokens, then verified predictions with the large target model in parallel. Optimized draft model selection and acceptance criteria.
What I Built
Implemented speculative decoding to accelerate autoregressive generation by 2-3x without quality loss. Used a small draft model to predict future tokens, then verified predictions with the large target model in parallel. Optimized draft model selection and acceptance criteria.
Key Concepts
Speculative DecodingDraft ModelToken VerificationAcceptance CriteriaSpeedupQuality Preservation
Architecture
1
Draft Model2
Target Model3
Speculation Engine4
Verification Pipeline5
Acceptance FilterResults
Achieved 2.8x speedup on text generation with identical output distribution. Draft model acceptance rate of 78% on news articles.
Key Learnings
- Speculative decoding is theoretically elegant and practically effective
- Draft model quality matters more than draft model speed
- Acceptance rate varies significantly by domain and prompt
Challenges
- Training an effective draft model
- Handling verification failures gracefully
- Optimizing speculation length dynamically