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arXiv · 2607.08930

BlockServe: Block-Grained Continuous Batching for High-Throughput Diffusion LLM Serving

Yuanjie Zhu·Liangwei Yang·Ke Xu·Weizhi Zhang·Shanghao Li·Zihe Song·Philip S. Yu
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Abstract

Efficient serving of diffusion large language models (dLLMs) is hindered by convergence heterogeneity: when batching multiple requests, different sequences converge at different rates, causing faster requests to stall behind slower stragglers and introducing compute bubbles and tail latency. We present BlockServe, a continuous batching framework that integrates block-grained scheduling -- immediately evicting completed requests at block boundaries -- with mixed-state execution that extends dual cache and parallel decoding to heterogeneous batches via gather-scatter indexing. Furthermore, a compute-aware admission controller expands effective batch capacity through token-budgeted refill. On Dream and LLaDA across five benchmarks, BlockServe achieves 1.9--10.6$\times$ throughput over Fast-dLLM with comparable generation quality, establishing block-grained scheduling as a foundation for high-throughput offline dLLM inference.

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Yuanjie Zhu, Liangwei Yang, Ke Xu, Weizhi Zhang, Shanghao Li, Zihe Song, Philip S. Yu. 2026-07-09. BlockServe: Block-Grained Continuous Batching for High-Throughput Diffusion LLM Serving. https://arxiv.org/abs/2607.08930

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