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 line covers.
Focused engineering with clear outcomes, documented implementation, and a path for your 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.
Three ways to bring the capability in.
Fixed-scope project, monthly retainer, or staffed capacity embedded with your program.
Defined outcome, defined price
Scope, deliverables, and exit criteria agreed before work starts.
Reserved monthly capacity
Ongoing operations and expertise without a new contract per task.
Engineering on-program
Embedded delivery, including cleared environments where required.
Ready to scope this work?
Tell us the environment, requirement, and timeline.