Speculative Decoding：速度背後的機制與邊界
推測解碼利用草稿模型預測多個詞元，再由目標模型並行驗證。這在特定條件下能減少延遲，但接受率與硬體邊界決定了真實收益。本文拆解核心機制、效能取捨與維運考量。
Inference
Speculative Decoding: The Mechanisms and Boundaries Behind Its Speed
Speculative decoding uses a draft model to predict multiple tokens, which the target model then verifies in parallel. Under certain conditions, this can reduce latency, but acceptance rate and hardware limits determine the actual gains. This article breaks down the core mechanism, performance trade-offs, and operational considerations.
Speculative Decoding Implementation Guide: Benchmarks, Validation, and Trade-offs
Validate model inference acceleration with Speculative Decoding. This article provides a complete process for building a baseline, introducing a Draft Model, validating acceptance rates and end-to-end latency, ensuring output-distribution consistency, and handling fallback mechanisms.
Speculative Decoding 實作指南：基準、驗證與取捨
透過 Speculative Decoding 驗證模型推理加速效果。本文提供建立基準、導入 Draft Model、驗證接受率與端到端延遲的完整流程，確保輸出分布一致性並處理回退機制。