Consistency diffusion language models: Up to 14x faster inference without sacrificing quality
Source
www.together.ai
Date
Why it matters
If you're deploying diffusion language models, CDLM removes their two biggest practical blockers — no KV caching and excessive refinement steps — via a post-training recipe that yields up to 14.5x lower latency without quality loss, making diffusion LMs viable for real inference workloads.
CDLM fixes both with a post-training recipe that enables exact block-wise KV caching and trajectory-consistent step reduction — delivering up to 14.5x latency improvements
Transcript
Standard diffusion language models can't use KV caching and need too many refinement steps to be practical. CDLM fixes both with a post-training recipe that enables exact block-wise KV caching and trajectory-consistent step reduction — delivering up to 14.5x latency improvements
