When discussing AI inference infrastructure, the word “scaling” can mean several very different things. We might need more GPUs because a model is too large for a single GPU, more copies of a model because concurrency is increasing, or better routing between those copies because repeated context is consuming unnecessary compute. Eventually, we may also […]

Read More

As AI workloads become increasingly central to business innovation, organizations are turning to modern infrastructure platforms that can scale AI training and inference reliably, securely, and efficiently. Two leading options in this space—VMware Cloud Foundation and Red Hat OpenShift AI—offer enterprise-grade solutions, but with very different philosophies and strengths. In this blog, we’ll explore the […]

Read More