# diffusion LLM

Flash-dLLM is a training-free inference acceleration framework for diffusion large language models: it pairs an I/O-aware fused KV-cache kernel — folding QKV projection, RoPE and cache writes together in SRAM and writing keys and values straight into the cache — with scheduled Flash Attention to ease GPU memory-traffic bottlenecks, keeps only a small set of most-attended tokens through selective cache updates, and lets the dLLM serve as both drafter and verifier (Flash-Verify); evaluated on LLaDA-1.5 across GSM8K, MATH, HumanEval and MBPP, it reports 5.1× and 11.0× speedups over the strongest baseline Elastic-Cache on GSM8K and HumanEval while preserving generation quality.

Flash-dLLM is a training-free inference acceleration framework for diffusion large language models: it pairs an I/O-aware fused KV-cache kernel — folding QKV projection, RoPE and cache writes together in SRAM and writing keys and values straight into the cache — with scheduled Flash Attention to ease GPU memory-traffic bottlenecks, keeps only a small set of most-attended tokens through selective cache updates, and lets the dLLM serve as both drafter and verifier (Flash-Verify); evaluated on LLaDA-1.5 across GSM8K, MATH, HumanEval and MBPP, it reports 5.1× and 11.0× speedups over the strongest baseline Elastic-Cache on GSM8K and HumanEval while preserving generation quality.