AI & GPU Platforms
From racked GPUs to running workloads — the layer AI actually depends on, engineered with fleet discipline.
Models get the attention; platforms determine whether they run.
AASHU delivers GPU infrastructure with the same discipline as the rest of the Linux estate: driver stacks managed as lifecycle, GPUs scheduled through Slurm or Kubernetes, distributed workloads enabled with MPI, and utilization visible to the people paying for it.
What this capability covers.
Focused engineering with clear technical outcomes, documented implementation, and a path for the operating team to own the result.
GPU node deployment
Driver stack, CUDA runtime, and firmware lifecycle across GPU server fleets.
Slurm GPU scheduling
GPU-aware Slurm partitions, GRES configuration, job allocation, accounting, and scheduling policy for shared accelerator clusters.
MPI & distributed compute
MPI-enabled multi-node workloads, launcher integration, fabric-aware configuration, and validation for distributed CPU/GPU jobs.
Kubernetes for GPU
NVIDIA GPU Operator, device plugins, and scheduling so GPUs are shared fairly.
GPU partitioning (MIG)
Multi-Instance GPU configuration matching hardware slices to workload profiles.
Inference serving
Model-serving infrastructure stood up and operated — runtime to monitored endpoint.
GPU observability
DCGM metrics into Prometheus/Grafana: utilization, memory, thermals, and per-job attribution.
AI cluster readiness
Racked hardware → gap analysis → working platform: OS, fabric, orchestration, scheduling, and monitoring.
What you receive.
From environment understanding to operational ownership.
Every engagement is scoped to the actual environment. The implementation changes by capability, but the operating discipline remains consistent.
Assess the environment
Map the current state, dependencies, constraints, risk, and operational ownership before making changes.
Engineer the implementation
Build the technical path with repeatable configuration, validation, and rollback appropriate to the scope.
Leave an operating model
Document the baseline, runbooks, lifecycle tasks, and handoff so the capability remains supportable.
Ready to scope this work?
Tell us the environment, requirement, constraints, and timeline.