Co-occurrence Patterns of LoRA Adapters in Production Diffusion Model Inference Services
Low-rank adaptation (LoRA) has become a key technology for serving large-scale personalized large language models and diffusion models in the cloud. However, the co-occurrence patterns, resource contention relationships, and evolutionary regularities of adapters under production inference workloads have not been systematically or quantitatively studied. Based on GenTD26, Alibaba's production diffusion model inference dataset, this paper adopts a graph-theoretic framework to construct an adapter co-occurrence network and conducts a characterization from both static structure and dynamic evolution. Our main findings are as follows. (1) The co-occurrence network is extremely sparse, and adap...
| Authors | Tao Zhang, Bin Liao, Tao Zhou et al. |
|---|---|
| Primary category | cs.DC |
| Published | 20 September 2026 |
| Last updated at source | 20 September 2026 |
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