Huibing Dong posted a 30‑minute video that walks through a paged‑attention case study on Google’s TPU, accompanied by a detailed blog post video blog. The tweet announcing the material links to both resources tweet.
Paged attention on TPUs – the core idea
Paged attention breaks the quadratic cost of classic self‑attention by processing variable‑length chunks (“pages”) that fit in on‑chip memory. The approach lets the model scale to longer sequences without exploding memory usage.
The blog walks through the kernel modifications required for the TPU’s systolic array, showing how ragged tensors are tiled and streamed to keep the hardware busy.
Performance and cost implications
In the video, Dong reports up to a 2.5× speed‑up on a TPU v4 compared with dense attention for 8K‑token inputs, while keeping latency under 50 ms. Those gains translate into lower per‑inference cost when running at scale on Google Cloud.
However, TPU usage is billed per second, and the high‑throughput configuration used in the demo costs roughly $0.40 per hour for a v4‑8 pod. Startups must balance the hardware expense against the potential reduction in model size.
Limitations and lock‑in risks
The implementation relies on custom XLA kernels that are not open‑sourced, so reproducing the exact performance on other accelerators (e.g., NVIDIA GPUs) is non‑trivial. Portability concerns may limit the technique to teams already invested in Google Cloud TPUs.
Additionally, the method adds complexity to the training pipeline; debugging ragged‑tensor bugs can generate noisy error messages that obscure the root cause.
When to experiment
If your workload already uses TPUs and you need to handle sequences longer than 4 K tokens, try the paged‑attention pattern on a small validation set before committing to production. The free video and blog provide enough detail to prototype without additional licensing costs.