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Geekbench AI is a benchmarking suite with a testing methodology for machine learning, deep learning and AI-centric workloads, all with the same cross-platform tooling and real-world workload reflection that our benchmarks are known for. Software developers can use it to ensure a consistent experience for their apps across platforms, hardware engineers can use it to measure architectural improvements, and anyone can use it to measure and debug device performance with a variety of tasks based on how devices actually use AI.

Geekbench AI measures CPU, GPU, and NPU to determine if your device is ready for today’s and tomorrow’s cutting-edge machine learning applications. It includes both computer vision and language tests that model real machine learning tasks and applications.

What’s new

Geekbench AI 1.0 includes other significant changes to improve the ability to measure real-world performance based on how applications use AI. This includes support for new frameworks, from OpenVINO on Linux and Windows to vendor-specific TensorFlow Lite delegates such as Samsung ENN, ArmNN and Qualcomm QNN on Android to better reflect the latest tools available to engineers and the changing ways developers build their apps . services on the latest hardware.

This release also uses more extensive datasets that more closely reflect real-world inputs in AI use cases, and these larger and more diverse datasets also increase the effectiveness of our new accuracy evaluations. All workloads in Geekbench 1.0 run for a minimum of one full second, altering the impact of vendor- and manufacturer-specific performance tuning on scores, ensuring devices can reach their maximum performance levels during testing while still reflecting the explosive nature of real-world use cases .

More importantly, this also better accounts for the delta in performance we see in real life; a five year old phone is going to be MUCH slower at AI workloads than say a 450W dedicated AI accelerator. Some devices can be so incredibly fast at some tasks that too short a test counterintuitively puts them at a disadvantage, under-reporting their actual performance in many real-world workloads!