CoLLM: Continuous Adaptation for SLO-Aware LLM Serving on Shared GPU Clusters
As Large Language Models (LLMs) are increasingly adopted in edge intelligence to power domain-specific applications and personalized services, the quality and efficiency of the LLM post-training phase-including fine-tuning and inference, have become critical due to constrained resources. Although recent advances in federated parameter-efficient fine-tuning (FL PEFT) and low-latency inference have improved individual task performance, fine-tuning and inference are still handled as isolated workloads, which overlooks their interdependence and results in redundant deployments and delayed improvement in inference quality. To address these limitations, we introduce a new co-execution framework...
| Authors | Shaoyuan Huang, Yunfeng Zhao, Na Yan et al. |
|---|---|
| Primary category | cs.DC |
| Published | 31 March 2026 |
| Last updated at source | 15 May 2026 |
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