Varun Mohan: evaluation

Transcript context

…yeah, yeah. Maybe for the audience, you wanna tell a bit about exa function, how that came to be and how coding came out of that. So a little bit about exo function. Before working at exa function, I worked at Neuro as Sean was just saying, and at neuro, I sort of managed large scale offline deep learning infrastructure. Realized that deep learning infrastructure is really hard to build and really hard to maintain for even the most sophisticated companies, and started exa function to basically solve that gap, to make it so that it was much easier for companies. To serve deep learning workloads at scale. One of the key issues that we noticed is GPUs are extremely hard to manage fundamentally because they work differently than CPUs. And once a company has heterogeneous hardware requirements, it's hard to make sure that you get the most outta the hardware. It's hard to make sure you can get, get great GPU utilization and exa function was specifically built to make it so that you could get the most outta the hardware. Make sure. Your GP was effectively virtualized and decoupled from your workload to make it so that you could be confident that you were running at whatever scale you wanted without burning the bank. Yeah. You gave me this metric about inefficiency,…

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