Sangam system optimizes serving for diffusion language models

Researchers have developed Sangam, a new serving system designed to efficiently handle diffusion language models (dLLMs). Unlike traditional autoregressive models, dLLMs generate text iteratively and have bidirectional attention, which complicates standard caching techniques. Sangam introduces a deficit token-budget scheduler to manage in-flight decodes and whole prefills, aiming for amortized stall-free scheduling. The system also employs a hybrid serving strategy to balance prefill and decode resource allocation, showing latency improvements over existing methods on benchmarks like LLaDA-8B and Dream-7B. AI

IMPACT Optimizes inference for diffusion language models, potentially reducing latency and improving efficiency for these computationally intensive systems.

RANK_REASON The item is a research paper detailing a new system for serving diffusion language models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• dKV-Cache
• Dream-7B
• Fast-dLLM++
• Hugging Face
• LLaDA-8B
• Sangam
• ShareGPT

• infra
• paper

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