Consistency diffusion language models: Up to 14x faster inference without sacrificing quality
Summary
This article introduces Consistency Diffusion Language Models (CDLM), a post-training technique that speeds up diffusion language model inference by combining consistency-based multi-token finalization with block-wise KV caching, achieving up to 14.5x latency speedups on math and coding tasks. It discusses diffusion language models, block-wise decoding, and the hardware efficiency of CDLM, with results showing faster inference while maintaining quality. The work has practical implications for AI tooling and production-grade deployments.