PrismServe: Efficient LLM Serving on Heterogeneous Accelerator Clusters

Abstract:Serving large language models across heterogeneous accelerators is challenging because partitioning a replica across device types introduces communication overhead during autoregressive decoding, while deploying complete quantized replicas can degrade model quality. We present PrismServe, a serving system that combines request-level collaboration across heterogeneous hardware with a mixture of quantized experts derived from the same foundation model. Each expert executes within a group of same-type accelerators in a single node, while a two-level router selects an expert based on predicted response quality and then selects a replica according to load and prefix-cache locality. Implemented on unmodified vLLM, PrismServe achieves 2.0--2.6$\times$ the throughput of cross-node or cross-vendor full-precision pipelined deployments and reduces P99 time per output token by 55--65\% when serving Qwen3-14B on 48 accelerators from three vendors. Across Qwen3-8B, Qwen3-14B, and Qwen3-32B on five downstream tasks, PrismServe outperforms the best single quantization method by 2.0--4.3 points while remaining within 1.4 points of full-precision accuracy. Our code is available at this https URL

Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)

arXiv:2508.09204 [cs.LG]

(or arXiv:2508.09204v3 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2508.09204 Focus to learn more arXiv-issued DOI via DataCite

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