Accelerated compute is now a first-class architecture decision
GPU scheduling, model-serving throughput, and data-gravity around accelerators change capacity planning, cost modeling, and vendor strategy. Whether the workload runs in a hyperscaler, on NVIDIA-accelerated infrastructure, or on an HPE Private Cloud AI stack on-prem, the same governance and portability questions apply.
NVIDIA AI Enterprise patterns
NIM Microservices
Packaging models as NVIDIA Inference Microservices for standardized, containerized deployment across clouds and on-prem.
GPU Scheduling & Multi-Tenancy
MIG partitioning, Kubernetes GPU operators, and fair-share scheduling for shared accelerator pools.
NeMo & Model Pipelines
Training-to-serving pipelines, quantization, and TensorRT optimization for latency-sensitive inference.
HPE Private Cloud AI (PCAI) patterns
Turnkey On-Prem AI Stack
Pre-integrated compute, storage, and networking for organizations that must keep sensitive data on-prem or in a sovereign cloud.
Hybrid Bursting
Patterns for keeping inference and sensitive data local while bursting training workloads to public cloud when capacity is needed.
Lifecycle & Observability
Unified monitoring across HPE GreenLake-managed infrastructure and the AI workloads running on top of it.
Cloud GPU vs. NVIDIA on-prem vs. HPE PCAI
- Data residency or regulatory constraint: favor HPE PCAI or NVIDIA-accelerated on-prem infrastructure.
- Bursty or experimental workloads: favor hyperscaler GPU instances with NIM containers for portability.
- Steady-state, high-utilization inference: owned accelerated infrastructure amortizes faster than pay-per-use cloud GPUs.
- Multi-cloud portability is a requirement: standardize on NIM / containerized model serving regardless of where it runs.
— Viswa