In the early 2000s, VMware introduced the world to virtual servers that allowed IT to make more efficient use of idle server capacity. Today, Run:AI is introducing that same concept to GPUs running containerized machine learning projects on Kubernetes.
AI models are great — after all, they’re at the core of everything from voice assistants to datacenter cooling systems. But what isn’t great is the time and effort required to fine-tune them. The data sets ingested by production algorithms comprise hundreds (or millions) of samples and take powerful PCs up to weeks to process. New techniques promise to expedite model training, but not all of them are generalizable.