Accelerator Reliability and Failure Handling Accelerators are the heart of modern AI infrastructure, but they are not “set and forget” devices. They are high-power, high-density computers packed with fast memory, complex interconnects, and firmware layers that have to behave correctly under extreme load. When a GPU or other accelerator fails, the impact is rarely a […]

Cluster Scheduling and Job Orchestration A GPU cluster is a shared system with competing goals: high utilization, predictable delivery, fair access, and controlled cost. Scheduling and orchestration are the mechanisms that reconcile those goals. They decide who runs, where they run, what resources they get, and what happens when the system fails or demand spikes. […]

Multi-Tenancy Isolation and Resource Fairness Multi-tenancy is what turns AI compute from a lab asset into shared infrastructure. It is the difference between a single team owning a dedicated cluster and many teams, customers, or workloads sharing the same fleet. Done well, multi-tenancy lowers unit cost, increases utilization, and makes capacity more flexible. Done poorly, […]

RDMA and GPUDirect: Zero-Copy Data Paths and Tail Latency When AI systems scale, moving bytes becomes the hidden tax that controls cost and latency. The system can have powerful accelerators and still feel slow because data takes too many hops, too many copies, and too many kernel transitions. RDMA and GPUDirect are families of techniques […]

Virtualization and Containers for AI Workloads AI workloads are unusually sensitive to environment details. A small mismatch in driver versions, runtime libraries, or kernel settings can turn a working system into an intermittent failure. At the same time, AI infrastructure is increasingly shared: multiple teams, multiple models, mixed priorities, and heterogeneous hardware. Virtualization and containers […]