MTServe: Efficient Serving for Generative Recommendation Models with Hierarchical Caches
arXiv cs.LG / 4/28/2026
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Key Points
- The paper introduces MTServe to reduce the high inference cost of generative recommendation by avoiding repeated encoding of long user histories through cross-request KV cache reuse.
- It addresses the “storage explosion” problem caused by massive per-user state sizes by virtualizing GPU memory and using host RAM as a scalable backup tier.
- MTServe improves performance between GPU and host tiers with a hybrid storage layout, an asynchronous data transfer pipeline, and a locality-driven cache replacement policy.
- Experiments on both public and production datasets show up to 3.1× speedup while preserving very high KV cache hit ratios above 98.5%.
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