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Shared Computing Power
Intelligent Future
10x+
General Computing Growth
500x+
AI Computing Growth
27%+
Global Data Share
105
ZFLOPS Computing Power
MicroCalc Disaggregated Computing Solution
Intelligent disaggregated computing system, designed specifically for high-performance computing environments, providing exceptional scalability and resource utilization
Disaggregated Computing System Architecture
Computing Cluster Layer
AI training, large-scale parallel computing
Resource Scheduling Layer
Unified scheduling of computing resources, load balancing
Storage Hardware Layer
Tiered storage for hot and cold data
Network Layer
200G high-speed computing network
Supporting Software
AI platform, resource management
Computing Cluster Layer
Utilizing IW4221-8GRs, IW4221-8GR, primarily for providing large-scale parallel data processing capabilities required for AI training
Resource Scheduling Layer
Using IW2221-2GR for scheduling and load balancing of the entire backend computing resources
Storage Hardware Layer
Using IS4210-36 for massive cold data storage for AI training, and GP5016-2401 with NVMe SSD for hot data in AI training
Network Layer
Employing 200G IB high-speed computing network and gigabit Ethernet management network, ensuring high bandwidth and low latency for data computation
Supporting Software
Cluster management software implements resource virtualization, partitioning, resource scheduling, and data processing through an AI open platform
High Performance
Modular
Scalable
MicroCalc Technology Solutions
We provide comprehensive solutions from massive data storage to high-performance computing, meeting the needs of enterprises of all sizes
Disaggregated Computing Center Solution
Designed for large enterprises and research institutions, achieving higher resource utilization and more flexible scalability
Learn More
Minimal System "Cube" Solution
Designed for SMEs, providing dedicated high-performance computing resources with low barriers to entry and excellent performance
Learn MoreAdvantages of Disaggregated Architecture
Faster! More Time-Efficient!
Accelerate Model Preparation
Quickly read large files and scattered files, significantly reducing data preparation time
Accelerate Model Training
Program random read/write I/O time reduction, overall training process more efficient
Digital China Construction
Facing Digital China strategy, MicroCalc provides innovative solutions to address explosive data growth
Digital China construction utilizes information technology and digitalization to improve government governance, promote economic growth, and enhance social welfare, driving China to become a harmonious society with a digital economy.
Based on digital technology, driving economic innovation and development through the Internet, big data, artificial intelligence and other technical means; promoting informatization, intelligence and convenience in all areas of society through the popularization and application of network platforms.
Digital Transformation Brings New Data Challenges - Explosive Data Growth
Currently, China's data generation accounts for 13% of global data, expected to reach 27% or more by 2025, becoming the world's leader; at the same time, with the rapid development of 5G, IOT, AI, the metaverse, and big data, data is developing explosively.
Digital Transformation Brings New Data Challenges - Computing, Storage, and Network Coordination
Digital Transformation Brings New Challenges to Data and Computing
- •Data production and collection facilities are complete, and data scale and content continue to grow.
- •Computing resources and methods are growing rapidly, and data-driven intelligence is gradually being implemented.
- •According to IDC predictions, China's data scale is expected to grow from 18.51ZB in 2021 to 56.16ZB in 2026, with a growth rate of 203.4%, ranking first in the global growth rate.
Digital Transformation Makes Large-Scale Computing Trend
The effective coordination of computing power, storage power, and network power will become a new challenge.
- •Data, computing power, and algorithms are the three essential elements of AI technology development, and data becomes an important asset.
- •"Storing and using" puts a severe challenge to computing infrastructure.
- •Resource scaling on demand drives architecture revolution, with "storage-computing separation" and "storage-computing integration" becoming increasingly unified internally.
"Storage power is the comprehensive capability of data centers in four aspects: data storage capacity, performance, security, and green and low-carbon. In the digital economy background, storage power is the key indicator to support big data era."
——来源工信部《中国存力白皮书》
According to GIV data statistics, by 2030:
- General computing power (FP32) will grow 10 times, reaching 3.3 ZFLOPS
- AI computing power (FP16) will grow 500 times, reaching 105 ZFLOPS
注：1YB=1024*1024*1024*1024TB
China's Data Share
0%
In 2025, China will generate over 27.8% of global data
Computing Power Growth Rate
0%
China's computing power grows at 67% annually, requiring innovative architecture support
AI Investment Scale
0B+
China's annual investment in AI infrastructure has exceeded 10 billion RMB
By 2025, China is expected to generate more than 27.8% of global data, with an annual data volume exceeding 48.6ZB, creating a surge in demand for high-performance computing and storage.
Data Growth Trend (ZB)
Technology Comparison
A comprehensive comparison of features and advantages across different technical solutions to help you choose the most suitable option
| 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
MicroCalc Technology, Providing Unlimited Computing Power for Your Data
Disaggregated architecture provides efficient and flexible infrastructure support for AI training and big data analysis