SLO-Aware Compute Resource Allocation for Prefill-Decode Disaggregated LLM Inference
Prefill-Decode (P/D) disaggregation has emerged as a widely adopted optimization strategy for Large Language Model (LLM) inference. However, there currently exists no well-established methodology for determining the optimal number of P/D hardware resources, subject to constraints on total throughput, service level objectives (SLOs), and request characteristics - specifically input and output lengths. To address this gap, we propose a hybrid approach that combines theoretical modeling with empirical benchmarking. First, we present a theoretical model for calculating P/D resource counts, which is based on total throughput requirements, request input and output lengths, as well as prefill an...
| Authors | Luchang Li, Dongfang Li, Bozhao Gong et al. |
|---|---|
| Primary category | cs.DC |
| Published | 5 March 2026 |
| Last updated at source | 5 March 2026 |
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