The GPU is busy. The cluster is busy. The queue is busy. But who is actually consuming the compute, and what is that consumption costing the organization? That question gets harder when one infrastructure serves HPC simulations, AI workloads, remote engineering labs, VDI, and containerized applications. A single GPU may run a Slurm job overnight,... Continue Reading →
Across enterprises, research organizations, and education institutes, one shift is unmistakable: organizations no longer want to simply rent AI they want to own it. Dedicated AI Labs are becoming core institutional infrastructure, and Sovereign AI the ability to develop, deploy, and scale AI without reliance on third-party cloud infrastructure or external governance has become a... Continue Reading →
For CFD and FEA workloads, cluster design is not just about adding more cores. It is about matching hardware to solver behavior, building the right node types, sizing memory correctly, choosing the right network and storage, and then managing the entire environment in a way that is easy for users and efficient for administrators. This... Continue Reading →
Introduction As AI, ML, and HPC workloads grow, efficient GPU utilization has become critical. Traditionally, a single GPU served one workload at a time, often leaving resources underused. To solve this, NVIDIA introduced Multi-Instance GPU (MIG) technology. MIG allows a single GPU to be securely partitioned into up to seven fully isolated GPU instances for... Continue Reading →
