Speculative decoding is the unsung hero of fast AI interfaces. Instead of relying solely on a massive model to generate every single token, a much smaller and faster 'draft' model predicts the next few tokens in advance. The large model then verifies these candidates in a single parallel step.
Since verifying tokens in parallel is computationally cheap compared to auto-regressive generation, this technique yields speedups of 1.5x to 2.5x without any degradation in quality. If the target model rejects the draft model's predictions, it simply corrects them, ensuring identical output.
As edge computing grows, speculative decoding is finding new use cases. Mobile devices and personal computers can run draft models locally, calling cloud APIs only for verification and corrections, creating high-speed hybrid architectures that preserve battery life and minimize latency.
