WavePP: High-Throughput Pipeline Parallel LLM Prefill under Prefix Reuse
Pipeline parallelism can improve prefill throughput by processing multiple request chunks concurrently across different stages of the model. However, keeping the pipeline fully utilized requires efficient scheduling and request preparation. In systems where stages retain and evict cache state independently, a local cache hit does not guarantee that the same prefix can be reused across the pipeline. Here, coordination overhead can impede request admission cadence and thus reduce overall throughput. In this paper, we present WavePP, a prefill runtime built on top of TensorRT-LLM that addresses these challenges by overlapping request admission with pipeline execution. WavePP asynchronously f...
| Authors | Aaryam Sharma |
|---|---|
| Primary category | cs.DC |
| Published | 28 September 2026 |
| Last updated at source | 28 September 2026 |
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