This research introduces Speculative Diffusion Decoding that significantly accelerates language model inference while maintaining output quality.

• Combines speculative decoding with discrete diffusion models to generate multiple tokens in parallel
• Overcomes limitations of traditional draft models that rely on incremental token generation
• Achieves superior computational efficiency compared to conventional speculative decoding methods
• Maintains the quality and coherence of generated text while reducing inference time

For engineering teams, this breakthrough enables faster LLM deployments without sacrificing output quality, potentially reducing computational costs and improving user experience in production applications.

Speculative Diffusion Decoding: Accelerating Language Generation through Diffusion