DeltaServe: Host-Agnostic Co-Serving of Inference and Fine-Tuning for LLMs
LLM serving systems are provisioned for peak load to meet strict latency targets, leaving substantial GPU compute idle whenever traffic falls below peak. We present DeltaServe, a host-agnostic co-serving design that converts this idle inference capacity into LoRA fine-tuning throughput while preserving inference service-level objectives (SLOs). DeltaServe integrates with existing inference engines through a compact hook interface that requires only multi-LoRA batching support. It exploits the shared execution structure of inference prefill and LoRA fine-tuning forward passes, and uses an SLO-aware scheduler to admit and execute fine-tuning only when sufficient inference headroom is availa...
| Authors | Jiaxuan Chen, Jianshu She, Ye Yuan et al. |
|---|---|
| Primary category | cs.DC |
| Published | 30 July 2026 |
| Last updated at source | 30 July 2026 |
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