Ragged Paged Attention: A High-Performance and Flexible LLM Inference Kernel for TPU
Large Language Model (LLM) deployment is increasingly shifting to cost-efficient accelerators like Google's Tensor Processing Units (TPUs), prioritizing both performance and total cost of ownership (TCO). However, existing LLM inference kernels and serving systems remain largely GPU-centric, and there is no well-established approach for efficiently mapping LLM workloads onto TPU architectures--particularly under the dynamic and ragged execution patterns common in modern serving. In this paper, we present Ragged Paged Attention (RPA), a high-performance and flexible attention kernel for TPUs, implemented using Pallas and Mosaic. RPA addresses these challenges through three key techniques: ...
| Authors | Jevin Jiang, Ying Chen, Blake A. Hechtman et al. |
|---|---|
| Primary category | cs.PF |
| Published | 16 April 2026 |
| Last updated at source | 16 April 2026 |
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