Decode-Branch Transformers: Decoupling the Primary Prefill Path from Additional Decode Computation
As large language models serve ever more requests, cumulative inference cost is growing relative to the one-time cost of training. In typical serving, prompt prefill runs in parallel and is compute-bound, whereas autoregressive decode is sequential and memory-traffic-bound. Conventional width or depth scaling raises both costs together, since every added layer is evaluated in both phases and enlarges the weights read at each decode step. We instead ask whether additional learned computation can be allocated to continuation prediction while preserving prompt-wide primary computation and a single KV cache. We realize this with the Decode-Branch Transformer. Its primary path alone processes ...
| Authors | Liming Liu, Mingze Wang, Tuo Zhao |
|---|---|
| Primary category | cs.AI |
| Published | 31 July 2026 |
| Last updated at source | 29 September 2026 |
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