Faster LLMs: Accelerate Inference with Speculative Decoding

Keypoints :

• Speculative decoding involves using a smaller draft model to generate multiple token predictions ahead of time.
• A larger target model verifies these predictions in parallel to ensure accuracy.
• The process includes three main steps: token speculation, parallel verification, and rejection sampling.
• Acceptance or rejection of tokens depends on comparing probabilities from both models, optimizing output quality.
• This technique allows generation of multiple tokens per model run, substantially increasing inference speed.
• The method improves GPU resource utilization and decreases computational costs without sacrificing output quality.
• Research continues to advance these optimizations, making LLMs more efficient and effective.