Speculative decoding speeds up generation by using a fast draft model to generate candidate tokens that a larger model verifies in parallel. When acceptance rates exceed seventy percent, overall token latency drops drastically without altering final output distributions. However, mismatched tokenizers or divergent draft training quickly turn this speedup into a performance bottleneck.
Aligning Draft and Target Distributions
Draft models trained on identical dataset distributions yield dramatically higher token acceptance across technical domains. If the draft model lacks domain-specific vocabulary, validation rejections force the primary target model to regenerate sequences sequentially. Fine-tuning the draft model on target output logs yields the single largest improvement in system throughput.
Optimizing Batch Sizes and Memory Overhead
Running two models concurrently increases memory overhead and requires careful VRAM allocation. Draft models should remain under three billion parameters to prevent GPU memory bandwidth contention with the main target model. Keep batch sizes small during speculative passes to maximize hardware memory bus efficiency.
Actionable Deployment Guidelines
Implement speculative decoding primarily for deterministic generation tasks like code completion or structured JSON extraction. Ensure draft models share exact tokenization schemes with the primary target model before measuring throughput gains.