SpecPilot raises speculative decoding speedup from 4.46x to 10.87x
Original title: SpecPilot: Autonomous Algorithm Discovery and Systems Optimization for Speculative Decoding
SpecPilot is an agentic loop that discovers algorithms and optimizations for speculative decoding across deployments.
SpecPilot addresses challenges in speculative decoding for LLMs including low token acceptance per step, halved speedup under batching, and lack of universal optimizations. An autonomous agentic loop locates gaps and integrates successful mechanisms via parallel agents and token forcing. It discovered a general lossless 12-bit weight format, tree verification for hybrid targets, two-level verification, and three batching mechanisms. Evaluated across four target models, three drafters, two GPU platforms and five benchmarks, it raises speedup from stock's 4.46x to 10.87x with one request.
- Discovered general lossless 12-bit weight format
- Tree verification for hybrid recurrent targets
- Two-level verification and three batching mechanisms
- Raises speedup from 4.46x to 10.87x with one request
- No training or quality loss required
From the original article
1 min read at huggingface.co
Speculative decoding accelerates memory-bound LLM decoding with a lightweight drafter, and parallel drafters extend this speedup by drafting a whole block in one forward pass. However, it remains unclear whether their released systems (stock) attain the full speedup that parallel drafting permits. We identify three challenges.
By Kunjun Li, Wenhao Chai, Yihao Liang · huggingface.co
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