专业 AI data center Solution
Integrating liquid cooling energy-saving technology (with PUE as low as 1.15), intelligent operation and maintenance systems, and modular high-density computing clusters, it reduces energy costs by 30%, supports elastic expansion, and helps enterprises build efficient and intelligent computing platforms.
Core challenges and requirements analysis.
The differences between AI computer rooms and traditional data centers
Ultra-high computing power density requirements
GPU/TPU clusters bring unprecedented computational density challenges.
- The power per single cabinet is 3-5 times that of a traditional data center.
- Space utilization requirements are extremely high.
- Requires a special heat dissipation solution.
High energy consumption and heat dissipation pressure.
AI training generates enormous amounts of heat, rendering traditional cooling solutions ineffective.
- Single GPU power consumption can reach 400-700W.
- Heat dissipation efficiency directly impacts computational performance.
- Electricity costs account for over 40% of operating costs.
Network latency sensitive
Distributed training imposes stringent requirements on network performance.
- Requires ultra-low latency GPU-to-GPU communication.
- High-bandwidth data exchange requirements
- Challenges in optimizing network topology structure
Dynamic scalability requirements
AI computing power demand is growing exponentially.
- Supports elastic scaling of computing power.
- Rapidly deploy new compute nodes.
- Flexible resource allocation capability.
Solution architecture
- Computing unit:Customized GPU/ASIC server clusters with horizontal scaling support.
- Storage system:Distributed storage + high-speed cache, meeting PB-level data throughput.
- Network architecture:100G/400G lossless networks, RoCEv2/InfiniBand low-latency interconnection
- Liquid cooling technology:Cold plate / immersion liquid cooling, PUE ≤ 1.2
- Smart temperature control:AI predictive temperature control, dynamically adjusting cooling strategies.
- Waste heat recovery:Optional waste heat power generation module to reduce operating costs.
- Dual utility power + diesel generator + energy storage UPS (99.999% availability)
- Modular power distribution design, supporting phased expansion.
- Real-time Power Monitoring and Intelligent Distribution System
- DCIM monitoring platform:Real-time tracking of energy consumption and equipment health status.
- AIOps Predictive Maintenance:Fault warning accuracy rate >95%
- Automation:Optional inspection robot, 7×24-hour unattended operation.
Industry-specific scenario adaptation
Internet enterprise
Supports training scenarios with GPU clusters at the thousand-card scale.
Financial AI
A low-latency network architecture that meets the demands of high-frequency trading.
Medical/Scientific Research
A quarantine solution that complies with biological data security regulations.
Edge AI node
Rapid deployment of miniaturized data centers.
Core competencies
Cost reduction and efficiency enhancement.
Liquid cooling technology reduces energy consumption costs by 30% and improves operational efficiency by 40%.
Fast delivery
Prefabricated modular data center, deployment cycle reduced by 50%.
Security compliance
Certified by Tier IV, supports compliance with Class 3 requirements of the Cybersecurity Multi-Level Protection Scheme 2.0.
Low-carbon pathway
Renewable energy integration solutions (photovoltaic/green power procurement)
Success stories
A certain autonomous driving company.
Deployed a 2000+ GPU cluster, optimized PUE to 1.15, and improved training efficiency by 35%.
Smart city project
Edge AI data center network latency is less than 0.1ms, with data processing speed improved by 50 times.
Financial institutions
Annual zero-downtime record, meeting millisecond-level transaction demands, with 100% security compliance.
Service
Needs assessment
Conduct an in-depth analysis of business scenarios, assess computing power requirements, and determine technical specifications.
Design approach
Customized architecture design, technology selection, and cost optimization solutions.
Hardware/software integration
Equipment procurement, system integration, software deployment and optimization.
Deployment and implementation.
Machine room construction, equipment installation, system commissioning, and stress testing.
Operations support
24/7 monitoring, regular maintenance, performance optimization, and capacity expansion support
Frequently Asked Questions
Here are the common questions about AI data center construction answered for you.
What are the differences between AI data centers and traditional data centers?
AI data centers are specifically designed for high-density computing power, with power per rack reaching 3-5 times that of traditional data centers, requiring advanced cooling technologies such as liquid cooling (with PUE as low as 1.15), and equipped with 100G/400G lossless networks to achieve low-latency communication between GPUs. In contrast, traditional data centers primarily rely on CPU computing power, with relatively lower requirements for cooling and networking.
Can liquid cooling technology really reduce PUE?
Yes. Liquid cooling technology can reduce cooling energy consumption by 30%-50% compared to traditional air cooling, with PUE dropping below 1.2 and reaching as low as 1.15. Liquid cooling also supports higher-density deployment (50kW+ per rack), and waste heat can be recovered and reused, further reducing operational costs.
How long does the construction cycle of an AI data center take?
Using a modular approach, the construction cycle for AI data centers can be shortened by 50% compared to traditional methods. Small AI computing nodes take approximately 4-8 weeks, medium-sized clusters about 3-6 months, and large supercomputing centers around 6-12 months. The specific timeline depends on scale, technology selection, and site conditions.
How can AI data centers ensure 99.999% availability?
Adopt a redundant power supply scheme of dual utility feeds + N+1 diesel generators + energy storage UPS, paired with an AIOps intelligent operations platform for fault prediction (accuracy >95%), combined with 7×24-hour monitoring and automatic switching mechanisms to ensure annual downtime does not exceed 5.26 minutes.
How many GPU clusters can an AI data center support?
The Juzhao Data AI Data Center solution supports elastic scaling from a single node to thousand-card clusters (1000+ GPUs), and even ten-thousand-card clusters. Through modular design and lossless network architecture, it achieves low-latency communication between GPUs, meeting the requirements of large model training.
Partners
Build top-tier solutions together with industry leaders.
Red Hat
A professional team tailors solutions for you, with free consultation and plan evaluation.
