Google explains how to use microbenchmarks to evaluate TPU performance
TPU benchmarks reveal real-world performance across workloads, architectures, and metrics See how Google’s microbenchmarks guide tuning, sharding, and faster deployments
Google has published a guide on using its microbenchmark suite to assess TPU performance more precisely than product specifications alone. The post, written by several Google software engineers and published on July 30, 2026, focuses on how the benchmarks can help developers measure realworld behavior across different TPU workloads and architectures.
The article breaks the suite into five areas: network communication, compute, high bandwidth memory, host transfers, and raggedpaged attention. It explains which metrics each category tracks, such as throughput, latency, TFLOPs, and bandwidth, and shows how those measurements can reveal whether a workload is computebound, memorybound, or networkbound.
Google also describes how the benchmarks connect to TPU tuning and system design, including sharding, kernel selection, rematerialization, and predictive modeling for larger deployments. A case study on an Ironwood TPU 7x training workload is included to show how benchmark results can guide performance improvements.