Technology Comparison
A comprehensive comparison of features and advantages across different technical solutions to help you choose the most suitable option
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MicroCalc Technology传统
Traditional Computing CenterDS
DeepSeek Framework| Feature | MicroCalc Technology | Traditional Computing Center | DeepSeek Framework |
|---|---|---|---|
1Architecture Design | Disaggregated System Excellent | Tightly Coupled Architecture Good | AI Model Optimization Good |
2Scalability | Very Strong, Horizontal Scaling Excellent | Weak, Requires Overall Planning Average | API-level Expansion Average |
3Resource Utilization | High Resource Pooling Excellent | Low Resource Allocation Flexibility Average | Computing Power via API Good |
4Network Performance | 200G IB High-speed Network, Low Latency Excellent | Traditional Network Architecture Good | Relies on Public Cloud Network Average |
5Flexibility | Computing and Storage Can Scale Independently Excellent | Overall Expansion, Low Flexibility Average | Fixed Model, Low Flexibility Good |
6Cost Performance | On-demand Allocation, Controllable Cost Excellent | Large Initial Investment, Idle Waste Average | Billed by Number of Calls Average |
7Application Scenarios | Large-scale AI Training and Inference Excellent | General Computing Tasks Good | Specific AI Model Inference Good |
8Data Processing Capability | Efficient Processing of PB-level Data Excellent | TB-level Data Processing Capability Average | Relies on Preprocessed Data Average |
9Hardware Utilization | Over 90% Excellent | 50-70% Average | Depends on Cloud Provider Good |
10Expansion Cost | Low, Independent Expansion on Demand Excellent | High, Requires Overall Upgrade Good | Medium, API Call Fees Average |
Leading Technology Architecture
Disaggregated system architecture provides optimal resource allocation and scalability, supporting large-scale AI training
Outstanding Performance
200G IB high-speed network and optimized storage architecture provide a low-latency, high-throughput computing environment
Optimized Cost Efficiency
On-demand resource allocation avoids the idle waste of traditional architectures, reducing total cost of ownership