BlockServe: Block-Grained Continuous Batching for High-Throughput Diffusion LLM Serving
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Computer Science > Machine Learning
Title:BlockServe: Block-Grained Continuous Batching for High-Throughput Diffusion LLM Serving
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.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2607.08930 [cs.LG] |
| (or arXiv:2607.08930v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.08930
arXiv-issued DOI via DataCite
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