Neural Magic, the AI company building a software platform for deep learning inference, announced a $30 million Series A funding round led by existing investor NEA with participation from Andreessen Horowitz, Amdocs, Comcast Ventures, Pillar VC, and Ridgeline Ventures. This financing brings the company’s total amount raised to $50 million. The new capital will be used to advance Neural Magic’s leadership in pure software machine learning acceleration and to support the success of a growing community of developers.
Born out of MIT, Neural Magic creates groundbreaking algorithms and tools that bring software, rather than specialized hardware, to the center stage in machine learning (ML) infrastructure. The company creates machine learning models that deliver GPU class performance on commodity CPU hardware, creating a flexible world of AI delivered and executed purely in software.
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“Earlier this year we made our model optimization tools and inference engine available to a community of machine learning developers, and it’s been exhilarating to watch their successes while learning from their feedback,” said Brian Stevens, Neural Magic’s CEO and former CTO of Red Hat and Google Cloud. “We will continue to listen, build, and innovate at the speed of software, toward our vision of an AI platform that fits as seamlessly into the cloud-native world as it does at the edge.”
Since open sourcing their machine learning tools earlier this year, Neural Magic has seen growing adoption across the machine learning community. Developers have been able to accelerate their model engineering timeline by starting with Neural Magic’s pre-optimized models and deploy more quickly without the constraints of specialty hardware.
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In conjunction with the financing, NEA’s Greg Papadopoulos will join Neural Magic’s board of directors. Papadopoulos brings vast experience to the team from his former role as CTO of Sun Microsystems, his mentorship of early-stage software companies, and his work at MIT on parallel dataflow computing architectures.
“Neural Magic has assembled a world-class team to tackle an incredibly challenging problem–delivering GPU speeds for machine learning inference (ML) using software and commodity CPUs,” said Greg Papadopoulos, Venture Partner at NEA. “The complexity of Neural Magic’s ML technologies and their ability to speed up the model optimization process while unlocking frictionless deployments are astonishing. We’re thrilled to continue partnering with the team to build a world where application inference can be deployed, managed, and scaled with zero friction.”
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