JointServe: Serving Multi-Model Agents on Memory-Constrained GPUs
LLM agents are evolving from a single model calling tools to planners that orchestrate multiple expert models whose capabilities live in distinct weights. This shift breaks a hidden assumption of today's serving systems: that the working set of models fits in GPU memory. On the memory-constrained GPUs typical of private and edge deployments, naive whole-model swapping serializes weight transfer with computation and, without host-backed KV restoration, discards session state at a switch. We present JointServe, a serving system that treats weight shards and KV blocks as residency objects competing for one unified memory budget. JointServe exploits three structural properties of agent worklo...
| Authors | Daijin Hu |
|---|---|
| Primary category | cs.DC |
| Published | 14 October 2026 |
| Last updated at source | 14 October 2026 |
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