Speculative decoding performance and costs measured on consumer systems
Execution-Path Qualification and Realized Costs in Speculative Decoding on Consumer Systems
Performance
Summary
Speculative decoding tries to predict the next steps of a computer program to work faster, but just matching early guesses doesn’t guarantee the whole prediction will be right. The authors tested a new prediction method on a consumer system and found it could match all tested cases but didn’t uniquely identify why mismatches happen. They also measured the time costs of their method, showing some slower steps within the prediction process. Their findings help engineers understand the trade-offs in prediction methods but don’t show a universal improvement in decoding speed or control.
What this means in practice
- •For software performance engineers: Evaluate speculative predictors in real systems by factoring in both prediction accuracy and the actual time costs measured on consumer hardware.
- •For embedded systems developers: Use detailed latency and cost measurements from speculative decoding to inform design choices in timing-sensitive embedded applications.
Authors
Chengzhan Li
Abstract
Numerical execution paths can change greedy output; a mismatch alone does not predict the remaining trajectory. On the tested B570 stack, we select a draft-free target-only micro-batch-one predictor using exposed discovery data, then freeze complete generated-ID predictions before reserved live speculation. It matches 72/72 capped sequences on 24 repeat-stable prompts, including all 12 runs of four reference-divergent prompts; the same-set reference matches 60/72. The predictor is sufficient for this path, but it neither restores qualification nor identifies a unique numerical cause. Separate B570 same-state forks test intervention effects; RTX proposal/replay programs test reproduction while sharing numerical routines. We assess cost separately. Newly direct-ID-qualified RTX serialization is slower within its instrumented post-prefill boundary ($0.646\times$ target/serialized latency). At fixed measured progress and target work, 358H remains below parity even with draft and residual costs removed. Qualified M4 Guard beats target-only but loses to fixed speculation. These results give testable diagnoses and engineering decisions about lossless compatibility and complete cost on the measured paths; they establish no portable decoding or control gain.