SUN: Shared Use of Next-token Prediction for Efficient Multi-LLM Disaggregated Serving
In multi-model LLM serving, decode execution remains inefficient due to model-specific resource partitioning: since cross-model batching is not possible, memory-bound decoding often suffers from severe GPU underutilization, especially under skewed workloads. We propose Shared Use of Next-token Prediction (SUN), the first approach that enables cross-model sharing of decode execution in disaggregated multi-LLM serving. SUN decomposes a decoder-only Transformer into a prefill module and a decode module, and fine-tunes only the task-specific prefill module, enabling a frozen decode module to be shared across models. This design enables a model-agnostic decode routing policy that balances deco...
| Authors | Sunghyeon Woo, Ahreum Seo, Jaegwang Lee et al. |
|---|---|
| Primary category | cs.AI |
| Published | 3 March 2026 |
| Last updated at source | 3 March 2026 |
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