AI & ML
Speculative Decoding
Quick Definition
An inference optimization where a small, fast 'draft' model rapidly predicts multiple tokens ahead, and a larger target model verifies them in parallel in a single forward pass.
Architectural Deep Dive
Standard LLM text generation is inherently memory-bandwidth bound because generating one token requires loading all model weights into GPU caches. Speculative decoding bypasses this bottleneck: a tiny 1B parameter model speculates 4 to 8 upcoming tokens in milliseconds. The massive 70B parameter model then evaluates all speculative tokens simultaneously in one forward compute pass. Valid tokens are accepted and inaccurate ones are discarded, doubling or tripling inference speed without any loss in output quality.
Why this matters in 2026: Produces 2x to 3x higher tokens-per-second generation speeds at zero loss in mathematical accuracy.
Common Engineering Pitfalls
- •Using a draft model with poor alignment to the target model, resulting in high rejection rates and slower speeds than baseline.