Strong Compute, a Sydney, Australia-based startup that helps developers remove the bottlenecks in their machine learning training pipelines, today announced that it has raised a $7.8 million seed round. The round includes a total of 30 funds and angels, including the likes of Sequoia Capital India, Blackbird, Folklore and Skip Capital, as well as Y Combinator, Starburst Ventures and founders and engineers from companies like Cruise, Waymo, Open AI, SpaceX and Virgin Galactic. The company, which was part of Y Combinator's Winter '22 batch, promises that its optimizations can speed up the training process by 10x to 1000x, depending on the model, pipeline and framework. As Strong Compute founder Ben Sands, who previously also co-founded AR company Meta, told me, the team has recently made some breakthroughs where it was able to take Nvidia's reference implementation, which its customer LayerJot used, to run 20 times faster. "That was a big win," Sands said.

Training neural networks takes a lot of time, even with the fastest and costliest accelerators on the market. It's maybe no surprise then that a number of startups are looking at how to speed up the process at the software level and remove some of the current bottlenecks in the training process. For Strong Compute, a Sydney, Australia-based startup that was recently accepted into Y Combinator's Winter '22 class, it's all about removing these inefficiencies in the training process. By doing so, the team argues that it can speed up the training process by 100x or more. "PyTorch is beautiful and so is TensorFlow. These toolkits are amazing, but the simplicity they have -- and the ease of implementation they have -- comes at the cost of things being inefficient under the hood," said Strong Compute CEO and founder Ben Sand, who previously co-founded AR company Meta (before Facebook used that name).