The infrastructure layer required to run AI workloads reliably — compute, GPU infrastructure, networking, storage, security, orchestration, monitoring, and operations — engineered by a team with nearly two decades of data centre operations experience.
AI workloads place specific demands on the infrastructure layer — many of which echo challenges we've solved before. Having operated physical data centre infrastructure across Asia, Africa, and Europe for nearly 20 years, we apply that same hands-on engineering discipline to design, deploy, and operate the environments that support AI inference and compute.
Server provisioning, capacity planning, and workload placement across physical and virtual compute resources.
GPU allocation, scheduling, driver and firmware management, and lifecycle operations for AI workloads.
High-bandwidth interconnects, low-latency paths, secure segmentation, and network observability.
Capacity, throughput, latency, durability, and cost-tiered storage for models, datasets, and checkpoints.
Model and data protection, access control, data residency, and supply chain integrity.
Workload scheduling, auto-scaling, infrastructure-as-code, and automated maintenance.
Full-stack metrics across compute, network, storage, and workload performance.
Reproducible provisioning, automated patching, and operational runbooks.
Day-2 operations for AI workloads — incident response, capacity planning, and optimization.
ViewRich's approach to AI infrastructure is grounded in nearly two decades of verified experience in data centre and IDC maintenance, cloud server deployment, and public cloud resource operations — across projects in Asia, Africa, and Europe.
