Despite the proliferation of accelerator chips like Google’s tensor processing unit (TPU) and Intel’s forthcoming Nervana NNP-T, most machine learning practitioners are limited by budget or design to commodity processors. Unfortunately, these processors tend to run sophisticated AI models rather slowly, exacerbating one of the many challenges involved in AI R&D.

Hence, Neural Magic. MIT Computer Science and Artificial Intelligence Lab research scientist Alex Matveev and professor Nir Shavit cofounded the Somerville, Massachusetts-based startup in 2018, inspired by their work in high-performance multicore execution engines for machine learning. The pair describes Neural Magic as a “no-hardware AI company,” in essence — one whose software processes workloads on processors at speeds equivalent to (or better than) specialized hardware.