AI Infrastructure
GPU drivers and runtime, AI frameworks, shared data, containers, monitoring and developer environments.
AI & HPC ENGINEERING
Compute platforms for
ambitious workloads.
Practical engineering across AI infrastructure, GPU compute, HPC scheduling, data systems and scientific software environments.
High Performance Computing
Cluster design, Slurm/PBS scheduling, MPI stacks, resource partitions and scientific computing workflows.
Operations & Enablement
Linux, Kubernetes, security-aware access, runbooks, support and team training.
DESIGN CONSIDERATIONS
Plan around the whole system.
WorkloadModel type, job patterns, concurrency and software dependencies.
ComputeCPU/GPU choices, memory capacity and scale.
FabricNetwork bandwidth, latency and node-to-node communication.
StorageCapacity, throughput, metadata and data lifecycle.
OperationsScheduling, monitoring, access, updates and troubleshooting.