Exafunction Raises $25 Million To Optimize ML With Virtualized GPUs
Exafunction plans to bring greater efficiency to deep learning setups with virtualized GPUs thanks to Series A funding round of $25 million led by Greenoaks.
Created on April 28|Last edited on April 28
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The machine learning industry is constantly growing, requiring more and more computational resources every day. As models get larger and more complex, they need more resources to train and run efficiently, and you'll ned to be shelling out more cash to keep up. Whether you're a hobbyist looking to get the most out of their home setup or a large company working with server rooms, Exafunction wants to make sure your setup is running as efficiently as possible to save costs and time.
Exafunction's primary service is an infrastructure for GPU virtualization, the ability to split the power of a single GPU across multiple devices or virtual machines. With GPU virtualization, you can make sure one process isn't bottlenecking the whole operation by hogging the GPU to itself, allowing for more efficient use of time and resources. With Exafunction, you can utilize the full processing power of your hardware for best results, up to 10x improvement in resource utilization and cost.
Who's funding Exafunction and where's the money going?
In a Series A funding round announced today, Exafunction raised $25 million. The funding effort was led by returning funder Greenoaks, as well as additional participation from Founders Fund. Exafunction plans to use the money in the expansion and deepening of their GPU virtualization product, as well as spreading their influence to acquire more customers and expand their staffing.
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