DyLLM: Efficient Diffusion LLM Inference via Saliency-based Token Selection and Partial Attention
DyLLM: Efficient Diffusion LLM Inference via Saliency-based Token Selection and Partial Attention explores DyLLM accelerates masked diffusion language model inference by selectively processing salient tokens, achieving significant throughput gains with minimal accuracy loss.. Commercial viability score: 7/10 in Efficient Inference.
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