CentML lands $27M from Nvidia, others to make AI models run more efficiently

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Contrary to what you mightve heard, the era of large seed rounds isnt over at least in the AI sector.CentML, a startup developing tools to decrease the cost and improve the performance of deploying machine learning models, this morning announced that it raised $27 million in an extended seed round with participation from Gradient Ventures, TR Ventures, Nvidia and Microsoft Azure AI VP Misha Bilenko.CentML initially closed its seed round in 2022, but extended the round over the last few months as interest in its product grew bringing its total raised to $30.5 million.The fresh capital will be used to bolster CentMLs product development and research efforts in addition to expanding the startups engineering team and broader workforce of 30 people spread across the U.S. and Canada, according to CentML co-founder and CEO Gennady Pekhimenko.Pekhimenko, an associate professor at the University of Toronto, co-founded CentML last year alongside Akbar Nurlybayev and PhD students Shang Wang and Anand Jayarajan. The highest-end chips are commonly unavailable due to the large demand from enterprises and startups alike. GPUs ability to perform many computations in parallel make them well-suited to training todays most capable AI.But there are not enough chips to go around.Microsoft is facing a shortage of the server hardware needed to run AI so severe that it might lead to service disruptions, the company warned in a summer earnings report. And Google hasnt managed to keep pace with demand for its cloud-hosted, homegrown GPU equivalent, the tensor processing unit (TPU), Wired reported recently.With spending on AI-focused chips expected to hit $53 billion this year and more than double in the next four years, according to Gartner, Pekhimenko felt the time was right to launch software that could make models run more efficiently on existing hardware.Training AI and machine learning models is increasingly expensive, Pekhimenko said. It has competitors in MosaicML, which Databricks acquired in June for $1.3 billion, and OctoML, which landed an $85 million cash infusion in November 2021 for its machine learning acceleration platform.But Pekhimenko asserts that CentMLs techniques dont result in a loss of model accuracy, like MosaicMLs can sometimes do, and that CentMLs compiler is newer generation and more performant than OctoMLs compiler.In the near future, CentML plans to turn its attention to optimizing not only model training but inference i.e. running models after theyve been trained.

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NVIDIA Corporation

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