Separate model-training and inference workloads
INFRASTRUCTURE
FactoryGPU
Private AI factory infrastructure — Builds on-premises GPU infrastructure around workload, network, storage, security and operating-runbook requirements rather than hardware count alone.
Concept · validation requiredValidate the complete network, storage and power path
Standardize operations, failure handling and backup procedures
COMPONENTS
Components
- GPU compute nodes
- Storage and network fabric
- Orchestration runtime
- Monitoring and backup runbook
INTEGRATION
Integration
- Model training and inference
- Data lake and storage
- Identity and backup
- DeviceOps
DELIVERABLES
Deliverables
- Capacity model
- Approved BOM
- Network and storage design
- Operating runbook
VALIDATION ITEMS
What must be validated before specifications
Performance, specifications and schedule are confirmed only after validating the actual samples and site conditions.
- 01Workload profile
- 02Data security
- 03Power and cooling
- 04Expansion plan
- 05Support and RMA terms
RELATED
Related solutions
Validate compute, I/O, thermal, security and trade compliance rather than selling GPU specifications alone.
EdgeBox AI
Validated real-time industrial AI node
Packages compute, I/O, thermal, power and software into an approved configuration matched to a specific factory workload.
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DeviceOps
Assets, OTA, rollback, SBOM and SLA operations
Manages the asset, version, deployment, patch, recovery and audit history of factory edge devices and software.
Learn moreFactoryGPU
Validate feasibility with actual samples and site conditions
Product status and specifications are stated in the proposal based on validation and supply conditions.