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

Magic, a startup founded by an MIT professor, who figured out a way to run 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 to run these jobs, but he says it doesn't have to be that way. He says his company, not only allows you to use this commodity hardware, it also works with more modern development approaches like containers and micro services.

“Our vision is to enable data science teams to take advantage of the ubiquitous computing platforms they already own to run deep learning models at GPU speeds in a flexible and containerized way that only commodity CPUs can deliver,” Shavit explained.

He says this also eliminates the memory limitations of these other approaches because CPUs have access to much greater amounts of memory, and this is a key advantage of his company's approach over and above the cost savings.

“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. This is the big unlock of Neural Magic,” he said.

Gil Beyda, Managing Director at lead investor Comcast Ventures sees a huge market opportunity with an approach that lets people use commodity hardware. “Neural Magic is well down the path of using software to replace high-cost, specialized AI hardware. Software wins because it unlocks the true potential of deep learning to build novel applications and address some of the industry's biggest challenges,” he said in a statement.

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