Consistency diffusion language models: Up to 14x faster inference without sacrificing quality
Hacker News
February 20, 2026
AI-Generated Deep Dive Summary
Consistency Diffusion Language Models (CDLMs) represent a groundbreaking advancement in AI, offering up to 14.5 times faster inference speeds without compromising on quality. Unlike traditional diffusion language models, which struggle with inefficiencies such as incompatible KV caching and excessive refinement steps, CDLMs address these bottlenecks through a post-training recipe that enables exact block-wise KV caching and reduces the number of required steps while maintaining high-quality output. This innovation makes iterative refinement processes significantly more efficient, unlocking new potential for parallel generation and higher throughput in language modeling tasks.
Standard diffusion language models rely on full bidirectional attention, which prevents the use of standard KV caching and forces models to recompute attention over the entire context
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Originally published on Hacker News on 2/20/2026