Consistency diffusion language models: Up to 14x faster, no quality loss
Consistency diffusion language models (CDLM) achieve up to 14.5x faster inference by utilizing consistency-based multi-token finalization and block-wise KV caching. These models provide a viable alternative to autoregressive language models for tasks such as math and coding.
together.ai
11 min
2/20/2026
Consistency diffusion language models: Up to 14x faster, no quality loss
Consistency diffusion language models (CDLM) achieve up to 14.5x faster inference by utilizing consistency-based multi-token finalization and block-wise KV caching. These models provide a viable alternative to autoregressive language models for tasks such as math and coding.
together.ai
11 min
2/20/2026
Consistency diffusion language models: Up to 14x faster, no quality loss
Consistency diffusion language models (CDLM) achieve up to 14.5x faster inference by utilizing consistency-based multi-token finalization and block-wise KV caching. These models provide a viable alternative to autoregressive language models for tasks such as math and coding.
together.ai
11 min
2/20/2026
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