Coral: Cost-Efficient Multi-LLM Serving over Heterogeneous Cloud GPUs
The usage of large language models (LLMs) has grown increasingly fragmented, with no single model dominating. Meanwhile, cloud providers offer a wide range of mid-tier and older-generation GPUs that enjoy better availability and deliver comparable performance per dollar to top-tier hardware. To efficiently harness these heterogeneous resources for serving multiple LLMs concurrently, we introduce Coral, an adaptive heterogeneity-aware multi-LLM serving system. The key idea behind Coral is to jointly optimize resource allocation and the serving strategy of each model replica across all models. To keep pace with shifting throughput demand and resource availability, Coral applies a lossless t...
| Authors | Yixuan Mei, Zikun Li, Zixuan Chen et al. |
|---|---|
| Primary category | cs.DC |
| Published | 5 May 2026 |
| Last updated at source | 5 May 2026 |
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