PRIVATE GPU
Private GPU Built Around the Workload
Move AI from experimental access to planned production infrastructure.
GPCN provides private NVIDIA GPU and supporting CPU infrastructure for development, sustained inference, training, RAG, machine learning, and other production AI workloads.
PRODUCTION AI
Getting a GPU Is Only the Beginning
AI requires more than just access. It requires privacy, consistent performance, data control and predictable economics.
GPCN delivers dedicated private GPU infrastructure with the supporting CPU, memory and storage needed to run AI workloads as a complete environment. With on-demand consumption, global availability and no egress fees across the network, organizations gain greater control without sacrificing flexibility.
GPU OPTIONS
Select the GPU for the Production Objective
NVIDIA A100 — 80 GB
A strong fit for established training, tuning and sustained inference workloads.
NVIDIA H100 — 80 GB
Designed for high-throughput transformer training and inference requirements.
NVIDIA H200 — 141 GB
Supports memory-intensive models, larger context windows and larger datasets.
NVIDIA RTX A6000 — 48 GB
An option for inference, vision and visualization workloads.
NVIDIA RTX PRO 6000 Blackwell — 96 GB
Supports Blackwell-based inference, RAG, simulation and media workloads.
GPU selection should begin with model memory, throughput, latency and workload behavior—not simply the newest accelerator name. Additional NVIDIA GPU options are available upon request.
DATA MOVEMENT
AI Starts with Compute. It Scales with Data.
AI performance depends on more than compute - it depends on where data lives, how efficiently it moves and the consistency of the infrastructure supporting it.
GPCN provides dedicated private GPU resources, giving organizations consistent access to the infrastructure their workloads are built around - not capacity that may be shared or dynamically reassigned. Combined with private CPU and storage, global availability and no GPCN egress fees, organizations gain greater control over their AI environment, data and infrastructure economics.
The result is a more predictable, consistent and controlled foundation for enterprise AI, from development through production.
Running AI Workloads With Predictable Cost and Control
Explore GPCN’s production AI infrastructure model, including private GPU and CPU infrastructure, data movement, workload fit and responsible sizing.
TAKE AI INTO PRODUCTION
What Does Your AI Workload Need to Run Reliably?
Bring us the workload, model requirements, target region and expected utilization. We’ll help coordinate the assessment.