GPU CLOUD · REFERENCE ARCHITECTURE
Enterprise AI Compute,built as one system.
A reference architecture showing how enterprise AI workloads run across the runtime, dedicated GPU compute, low-latency networking, and the data layer.
Integrated Compute Stack
ARCHITECTURE OVERVIEW
How AI compute is built
Enterprise AI compute integrates workloads, runtime, GPU capacity, networking, and storage into one production-ready architecture.
- 01
Demand Layer
AI Workloads
Training, inference, and enterprise AI applications define what the compute system needs to run.
- Training
- Inference
- Enterprise AI
- 02
Execution Layer
AI Runtime
The runtime coordinates scheduling, orchestration, and workload lifecycle across the compute environment.
- Scheduling
- Orchestration
- Lifecycle
- 03
Compute Layer
GPU Compute
Dedicated accelerated compute executes demanding enterprise AI workloads.
- Accelerated Compute
- Dedicated Capacity
- Enterprise Clusters
- 04
Fabric Layer
Networking
Low-latency networking connects distributed compute and data services as one system.
- Compute Fabric
- Data Movement
- Distributed Systems
- 05
Data Layer
Storage
The storage layer maintains datasets, checkpoints, and model assets used throughout the workload lifecycle.
- Datasets
- Checkpoints
- Model Assets
SYSTEM RELATIONSHIPS
Every layer has a clear role.
- 01
Workloads set demand
Training and inference requirements shape the runtime, compute, data, and network resources used by each deployment.
- 02
Runtime coordinates execution
Scheduling and orchestration connect application demand to the GPU resources that execute it.
- 03
Fabric connects the system
Networking and storage support the movement and persistence of data across distributed compute environments.
ENTERPRISE GPU COMPUTE