Achieving exponentially cheaper training time with just a few lines of changes in existing ML pipelines
The new science of compute and energy efficient training: mimicking the sparsity of the human brain
Using the BOLT Engine to train billions of parameter neural networks on old refurbished desktops
A 200 million parameter recommendation model can be trained on M1 laptops faster than A100 GPUs
Detailed comparison between the BOLT Engine and other standard machine learning alternatives
The time and cost to train models bottleneck traditional AI, but not BOLT