# How to use Google microbenchmarks for evaluating TPU performance

DevFeed: [How to use Google microbenchmarks for evaluating TPU performance](<https://devfeed.tech/articles/how-to-use-google-microbenchmarks-for-evaluating-tpu-performance-4213.md>)

Original publisher: [Read original article](<https://developers.googleblog.com/how-to-use-google-microbenchmarks-for-evaluating-tpu-performance/>)

Author: Junjie Qian; Chi Shuen Lee; Yu-Hsuan (Amy) Lin; Haixiong (Sean) Wang

Published: 2026-09-12T11:04:33.891311Z

Content type: tutorial

Language: en

Sources: [Google Developers Blog](<https://devfeed.tech/sources/google-developers-blog.md>)

Topics: [Google AI](<https://devfeed.tech/topics/google-ai.md>), [Inference Performance](<https://devfeed.tech/topics/inference-performance.md>)

Tags: [architecture](<https://devfeed.tech/tags/architecture.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [compute](<https://devfeed.tech/tags/compute.md>), [developers](<https://devfeed.tech/tags/developers.md>), [google](<https://devfeed.tech/tags/google.md>), [guides](<https://devfeed.tech/tags/guides.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [how-to](<https://devfeed.tech/tags/how-to.md>), [kernel](<https://devfeed.tech/tags/kernel.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [memory](<https://devfeed.tech/tags/memory.md>), [mesh](<https://devfeed.tech/tags/mesh.md>), [metrics](<https://devfeed.tech/tags/metrics.md>), [model](<https://devfeed.tech/tags/model.md>), [open](<https://devfeed.tech/tags/open.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [performance](<https://devfeed.tech/tags/performance.md>), [scale](<https://devfeed.tech/tags/scale.md>), [software](<https://devfeed.tech/tags/software.md>), [tpu](<https://devfeed.tech/tags/tpu.md>)

## AI overview

A tutorial on using Google's TPU microbenchmark suite to measure network, compute, memory, host-transfer, and attention performance. The results can establish a Roofline baseline and guide workload-specific optimization.

## Source excerpt

Google's open-source TPU microbenchmark suite provides developers with granular performance metrics across Network, Compute, HBM, Host Transfer, and Attention components to validate real-world hardware capabilities. By leveraging these benchmarks to establish a Roofline model, engineers can accurately diagnose whether their machine learning workloads are compute-, memory-, or network-bound. This empirical baseline directly guides targeted software optimizations--such as kernel tuning, mesh sharding, and rematerialization--to maximize hardware utilization for large-scale model deployments.