Inference Working Group


Create a set of fair and representative inference benchmarks.


Machine-learning (ML) hardware and software system demand is burgeoning. Driven by ML applications, the number of different ML inference systems has exploded. Over 100 organizations are building ML inference chips, and the systems that incorporate existing models span at least three orders of magnitude in power consumption and five orders of magnitude in performance; they range from embedded devices to data-center solutions. Fueling the hardware are a dozen or more software frameworks and libraries. The myriad combinations of ML hardware and ML software make assessing ML-system performance in an architecture-neutral, representative, and reproducible manner challenging. There is a clear need for industry-wide standard ML benchmarking and evaluation criteria. MLPerf™ Inference answers that call.


  1. Inference benchmark rules and definitions
  2. Inference benchmark reference software
  3. Inference benchmark submission rules
  4. Inference benchmark roadmap
  5. Publish inference benchmark results every ~6 months

Meeting Schedule

Weekly on Tuesday from 8:35-10:00AM Pacific.

How to Join and Access Working Group Resources

This group is limited to exclusively Members and Affiliates. If you are not already a Member/Affiliate or part of a Member/Affiliate company, you can learn more about Membership here.

Working Group Chairs

To contact all Inference working group chairs email

Miro Hodak ( - LinkedIn

Miro Hodak is a Senior Member of Technical Staff at AMD where he works on AI performance, strategy, and solutions. Before joining AMD, he worked as an AI Architect at Lenovo Infrastructure Solutions Group, and, prior to that, he was a Research Assistant Professor in Physics at North Carolina State University. Miro has participated in MLPerf/MLCommons activities since 2020 including submitting multiple rounds of Inference and Training benchmarks. Miro has journal publications spanning AI, computer science, materials science, physics, and biochemistry. His work has been cited over 2,000 times.

Mitchelle Rasquinha ( - LinkedIn

Mitchelle Rasquinha is a Senior Software Engineer working on the ML Performance Team within Google. She is interested in accurately capturing innovations in system architectures through robust benchmarking. Mitchelle has a background in Computer Architecture from the Georgia Institute of Technology.