Infrastructure & deployment
Co-Designing AI Models Using Speculative Decoding for Faster LLM Inference
Speculative decoding trades draft cost against target-model verification gains; the best draft length and mechanism depend on workload and position on the throughput–interactivity frontier.
Why it matters: A larger draft is not automatically faster. Benchmarking AL and draft overhead on representative tasks helps avoid trading away end-to-end speed or accuracy for a misleading single metric.