System Design for Production Diffusion LLM Serving with Limited Memory Footprint (arxiv.org)

Researchers have introduced a new serving system called dLLM-Serve, designed to tackle the "memory footprint crisis" associated with Diffusion Large Language Models (dLLMs). Unlike traditional autoregressive models, dLLMs use parallel decoding but often suffer from inefficiencies due to their resource-intensive memory dynamics. The research highlights how traditional optimizations have neglected the serving framework needed for efficient dLLM deployment in production environments. Key innovations in dLLM-Serve include Logit-Aware Activation Budgeting, a Phase-Multiplexed Scheduler, and Head-Centric Sparse Attention, all aimed at optimizing memory usage and computational scheduling. Significantly, dLLM-Serve demonstrates up to 1.81 times throughput improvement on consumer-grade GPUs like the RTX 4090 and nearly quadruples tail latency efficiency under heavy loads. This marks a breakthrough in practical dLLM inference, allowing for better resource utilization and performance across various hardware setups. By converting theoretical optimizations into real-world gains, dLLM-Serve sets a new standard for scalable dLLM applications, addressing a critical barrier for AI and machine learning workloads in production settings.