Haiyu Cloud Shenzhen Qianhai Intelligent Computing Center Hosting: 8kW Standard Cabinets and 30kW High-Power Solution Suited for GPU Servers

With the continuous development of applications such as large-model training, AI inference, and image generation, more and more enterprises are beginning to purchase GPU servers to build their own AI R&D environments or computing platforms. However, when deploying AI businesses, enterprises often focus first on GPU models, memory capacity, and computing performance, while easily overlooking whether the servers can be smoothly deployed after they are purchased.

Some enterprises, after purchasing multi-GPU servers, found that their existing data centers could provide only a few thousand watts of power, while a single new device consumes close to 10 kW, so the existing racks simply could not meet the requirements, and they had to look for a new colocation data center. Other AI R&D teams, limited by office space, need to place servers in professional data centers, while also hoping to control upfront investment and avoid paying too much cost from the start for computing power demand that has not yet been determined.

These problems show that GPU server selection cannot rely only on hardware configuration. Whether device power consumption, data center power supply, cooling conditions, and network capabilities can match one another directly affects whether the servers can be deployed and whether the invested computing power can be fully utilized.

I. Why can't high-power GPU servers be placed just because there is a rack?

Traditional server colocation usually starts by looking at device height, number of racks, and bandwidth. But for high-power GPU servers, the first thing to confirm is whether the data center can provide enough power and remove the heat generated by the devices during operation.

The number of rack units determines whether the equipment can be installed, while the actually available power determines whether the equipment can operate normally. Enterprises should calculate the power demand of all devices based on server models, overall power consumption, and deployment quantity, and confirm whether the power supply circuits, rack power distribution units (PDUs), and power redundancy meet the requirements. Looking only at whether there are empty slots in the rack, or simply adding up the rated power of the device power supplies, is not enough to determine whether the data center is suitable.

After power supply meets the requirements, cooling must also be considered. Most of the electrical energy consumed by servers is ultimately converted into heat, and the more concentrated the devices are, the more heat the data center needs to exhaust. If cold air cannot be effectively delivered to the device air intakes, or if the hot air exhausted by the devices cannot be dissipated in time, local overheating may occur inside the rack even if the overall data center temperature is normal.

At present, data centers mainly adopt cooling methods such as air cooling and liquid cooling. Air cooling removes heat through air flow; liquid cooling uses coolant to exchange heat with heat-generating components and is suitable for correspondingly designed equipment and high heat flux density scenarios. The specific choice depends on the server's cooling design, power consumption, and device density within the rack; not all GPU servers must use liquid cooling.

When choosing a data center, enterprises will also see the PUE metric, which reflects the relationship between the total energy consumption of the data center and the energy consumption of IT equipment. Good PUE performance does not mean that the rack's power and cooling capabilities necessarily meet the requirements; ultimately, it still depends on whether the data center can support the equipment planned for deployment.

If power consumption and cooling conditions are not calculated clearly in advance, and it is only discovered after the servers arrive at the data center that they cannot be racked, it may be necessary to find another data center and adjust the deployment plan, adding extra time and cost.

For single-machine inference, text-to-image, and image and video computing, in addition to the GPU's own computing capability, attention should also be paid to the read speed of models and data, as well as the network quality when users access the server. If storage read/write speed cannot keep up, the GPU may need to wait for data; if the external network is unstable, users may also encounter problems such as slow response and unsmooth operation.

For multi-node AI training, network and storage requirements are usually higher. When multiple GPU servers train collaboratively, data and training results need to be exchanged between computing nodes. If the network bandwidth between nodes is insufficient and transmission latency is too high, some GPUs may wait for other nodes to complete data exchange, slowing down overall training progress. At the same time, reading training data and saving model states during training also consume storage and network resources. Therefore, before deploying a multi-node training cluster, it is necessary to plan how nodes will connect, where data will be stored, and whether storage read/write speed can keep up with training requirements. Simply increasing the number of GPUs does not necessarily shorten training time proportionally.

For enterprises in the early stage of R&D, bandwidth adjustment and operations and maintenance support deserve more attention. When downloading models and updating datasets, network traffic may increase sharply in a short period, so enterprises should learn in advance whether bandwidth can be temporarily upgraded, what conditions are required for activation, and how related fees are calculated. After the equipment arrives, hardware inspection, racking and commissioning, network configuration, and subsequent fault handling may also be involved; whether the data center can provide corresponding on-site assistance will also affect the progress of business launch.

Multi-node training should prioritize planning of cluster network and storage; in the early stage of AI R&D, the flexibility of bandwidth adjustment, colocation cost, and operations and maintenance response should be key considerations. Clarifying what the business needs first, and then choosing the corresponding data center and colocation plan, is more meaningful than simply comparing rack prices.

For scenarios such as GPU servers, AI training and inference, and high-performance computing, Haiyu Cloud provides server colocation services at the Shenzhen Qianhai Intelligent Computing Data Center.

Haiyu Cloud's Shenzhen Qianhai Intelligent Computing Data Center is located in the core area of Qianhai, Nanshan District, Shenzhen. It is built according to national Class A and Tier 3+ standards, adopts hybrid air and liquid cooling, and is equipped with an intelligent environmental management system.

In terms of rack power supply, the data center normally supports 8 kW rack power and also provides customized high-power solutions above 30 kW, allowing the colocation environment to be planned according to the power consumption and cooling requirements of different devices. The data center's PUE is below 1.25, providing a reference for enterprises evaluating data center energy efficiency.

In terms of operations and maintenance, Haiyu Cloud provides 7×24-hour professional operations and maintenance duty, supporting basic services such as equipment racking and de-racking, hardware replacement, fault response, and remote assistance, helping enterprises handle practical issues in server deployment and daily maintenance.

In terms of network, full-rack colocation can be configured with public bandwidth, IPs, and dedicated lines as needed; for individually rented rack spaces, bandwidth and lines can be upgraded according to demand, including BGP lines. For enterprises involving overseas business, cross-regional offices, or multi-cloud interconnection, Haiyu Cloud's overseas dedicated lines, multi-cloud dedicated lines, and SD-WAN networking services can also be evaluated according to actual needs.

IV. Final thoughts

Before purchasing GPU servers, enterprises are advised to first clarify server models, total power consumption, computing tasks, storage requirements, and network architecture, and then, combined with future expansion plans, confirm the power, cooling, and network conditions that the data center can provide.

The key to GPU server deployment is not only buying hardware with sufficient performance, but also ensuring that data center conditions can keep up with business needs. Only by planning the matching of equipment and data centers in advance can problems such as equipment failing to be racked as planned, limited training efficiency, and subsequent expansion being hindered be reduced, so that computing power investment truly serves AI business.