Accelerating Spatio-Temporal Attention for Video Diffusion on TPUs- Google Developers Blog
Sep 30, 2026Ravisri Valluri, Sagar Chapara, Rishabh Manoj

Summary
This post details the acceleration of spatio-temporal attention for video diffusion models on TPUs by implementing sparse attention kernels using JAX and Pallas Splash Attention. It describes the transition from theoretical sparsity to practical end-to-end speedups by optimizing tile traversal strategies, avoiding intra-tile masking, and aligning sparse masks with execution tiles to reduce latency and improve efficiency on TPU v6e chips.
More from the JAX Model Evaluation Metrics trail
- Reproducing OLMo 3 7B Pre-training in MaxText: case study of large scale training on TPUs- Google Developers Blog
- Decoding cosmic signals with deep learning and Keras- Google Developers Blog
- Enterprise-Grade Precision for Long-Context Multimodal Embedding Inference on Cloud TPU- Google Developers Blog
- HeyGen x Google Cloud: Bringing Avatar IV to TPUs- Google Developers Blog
- Run Ray on TPU, Part 2: Ray AI libraries- Google Developers Blog