Benchmarking AI Models

Challenges in making proper comparisons

Benchmarking AI systems is a challenge. With many hardware types, and increasing number of models on the market, and other parts of the system like network and storage that can impact performance, figuring out the right system design is difficult.

To complicate the situation further, the standard benchmarking data sets for some data types aren’t really representative of real world AI workloads. That’s why our recommendation is to constantly test and evaluate AI models and hardware against your own real world needs and constraints.

Today we are release this ebook to show you how. It covers the questions to ask, best practices for AI benchmarking, and other key issues to consider. If you are running a benchmarking project and Neurometric can be helpful, please reach out.