Researchers have developed Sangam, a novel serving system designed to efficiently handle diffusion language models (dLLMs). Unlike traditional autoregressive models, dLLMs generate text through iterative denoising and cannot directly apply standard KV caching due to their bidirectional attention mechanism. Sangam addresses this by introducing a deficit token-budget scheduler that prioritizes in-flight decodes and admits whole prefills only when token budgets allow, ensuring amortized stall-free scheduling. The system also employs a hybrid serving strategy to manage prefill-decode resource partitioning, optimizing performance for both decode-heavy and prefill-heavy workloads. AI
IMPACT Introduces a new serving system that could improve the efficiency and latency of diffusion language models.
RANK_REASON Paper detailing a new system for serving diffusion language models. [lever_c_demoted from research: ic=1 ai=1.0]
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