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Neural Magic gets $15M seed to run machine learning models on commodity CPUs

Neural Magic, a startup founded by an MIT professor, who figured out a way to run machine learning models on commodity CPUs, announced a $15 million seed investment today.

The company also announced early access to its first product, an inference engine that data scientists can run on computers running CPUs, rather than specialized chips like GPUs or TPUs. That means that it could greatly reduce the cost associated with machine learning projects by allowing data scientists to use commodity hardware.

“Yes, running on a commodity processor you get the cost savings of running on a CPU, but more importantly, it eliminates all of these huge commercialization problems and essentially this big limitation of the whole field of machine learning of having to work on small models and small data sets because the accelerators are kind of limited.

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