1. What makes up the investment?
In on-premise deployment, hardware such as CPUs costs little; the main cost concentrates on GPUs (AI compute) and storage. Two GPUs can run AI but slowly; when AI speed matters, GPUs can be multiplied (e.g. 8 or 16 cards), with training efficiency improving accordingly.
2. How is the investment scale measured?
Forming baseline productivity requires at least 1-2 million RMB of AI capability investment; the supplier’s own full-configuration cluster investment exceeds 6 million RMB and still requires renting Alibaba Cloud compute for large-scale operations, showing how demanding large-scale AI training is on compute.
3. On-premise vs cloud investment comparison
| Mode | Investment | Applicable to |
|---|---|---|
| On-premise | From 1-2 million RMB | Data-sensitive customers |
| Cloud | Basic fee included in service charge | Lower one-time investment |
4. Applicable conditions and scenarios
- Customers with data sensitivity requiring local storage
- Professional organizations with high demands on AI training efficiency
- Customers with adequate budgets and routine high-frequency operations
5. Notes
On-premise hardware carries depreciation risk as AI technology evolves; evaluate comprehensively against business scale and cloud deployment costs.