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.

Comcast Ventures led the round with participation from NEA, Andreessen Horowitz, Pillar VC and Amdocs. The company had previously received a $5 million pre-seed, making the total raised so far, $20 million.

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.

The idea for this solution came from work by MIT professor Nir Shavit. As he tells it, he was working on neurobiology data in his lab and found a way to use the commodity hardware he had in place. “I discovered that with the right algorithms we could run these machine learning algorithms on commodity hardware, and that’s where the company started,” Shavit told TechCrunch.

He says that there is this false notion that you need these specialized chips or hardware accelerators to have the necessary resources

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