Support > About independent server > What are the specific requirements for AI large model servers?
What are the specific requirements for AI large model servers?
Time : 2025-06-17 16:43:39
Edit : Jtti

The development of AI big models has pushed higher requirements for server hardware and infrastructure. Especially in the fields of deep learning, natural language processing, image recognition, etc., the parameter scale, computational complexity and data throughput of big models continue to grow, and ordinary servers can no longer meet their training and reasoning needs. In order to efficiently support the development and deployment of AI big models, servers need to have higher specifications in terms of processor performance, memory capacity, storage speed, network bandwidth and heat dissipation efficiency, and also support flexible expansion and large-scale distributed computing architecture. For server procurement and use groups, understanding the key requirements of AI big model servers is the basis for ensuring the smooth development of business and improving R&D efficiency.

AI big models have extremely high performance requirements for processors. Unlike traditional general-purpose businesses, high-intensity matrix operations, vector calculations and high-dimensional tensor operations are required during AI training, so GPUs have become the core components of AI servers. Currently, the industry widely uses data center-level graphics cards such as NVIDIA A100, H100, and L40, which have tens of thousands of cores and hundreds of TFLOPS of computing power, which can greatly shorten model training time. For CPU, you need to choose models with high main frequency and large cache as scheduling and IO processing cores, including AMD EPYC and Intel Xeon series. In addition, AI large model servers need to support multi-GPU interconnection technologies such as NVLink and PCIe Gen4/Gen5 to ensure high-speed data exchange between multiple cards and improve distributed training efficiency.

Memory configuration also plays an important role in AI large model servers. Model parameters, activation values, intermediate feature maps, etc. require a large amount of memory as temporary storage space during training and inference. In general, the memory of AI servers must be at least 512GB, and high-end configurations can even be expanded to 1TB or higher. High-frequency DDR4 or DDR5 memory should be used to meet the data loading requirements of the GPU to avoid performance bottlenecks due to insufficient memory bandwidth. At the same time, in large distributed clusters, cache consistency and remote memory access optimization are also important factors in improving overall training efficiency. Therefore, the server should also support NUMA optimization and efficient memory scheduling across nodes.

In terms of storage, AI large models have high requirements for data reading and writing speeds, especially in scenarios such as loading, preprocessing, caching, and training logging of large data sets. To ensure the smooth flow of data pipelines, AI servers should be equipped with enterprise-level NVMe SSDs with high IOPS and high throughput to reduce data loading delays. In addition, in order to support PB-level data storage and access requirements, servers must also have high-capacity mechanical hard disks for cold data storage, as well as support for distributed file systems such as Ceph, BeeGFS, GlusterFS, etc., to achieve high availability and elastic expansion of data. For storage interfaces, it is necessary to ensure support for high-speed channels such as U.2 and M.2 to meet the access requirements of high-performance storage devices.

Network bandwidth and low-latency communication capabilities are also important components of AI large model servers. In single-machine multi-card or cross-node cluster training, the synchronization of model parameters and the aggregation of gradients require high-speed network support. Generally speaking, AI servers need to be equipped with 100Gbps or higher network cards and support high-performance network protocols such as RDMA and RoCE to reduce communication overhead and improve distributed computing efficiency. Some advanced clusters will also be equipped with Infiniband networks to further reduce latency and improve bandwidth utilization to ensure the horizontal expansion capabilities of large models.

Power, heat dissipation and computer room environment are also critical to the stable operation of AI servers. High-performance GPUs and multi-channel CPUs consume extremely high power. The power consumption of a single AI server is often between 2kW and 5kW, and must be matched with high-specification power supply design and rack wiring. At the same time, since AI training tasks will run at high load for a long time, the server needs to use efficient liquid cooling or customized air cooling solutions for heat dissipation to ensure that the hardware temperature is controlled within a safe range to avoid frequency reduction or hardware damage due to overheating. In addition, the computer room environment should also have high-redundancy power, environmental monitoring and security access control to ensure the long-term stable operation of AI infrastructure.

For users who use AI large model servers, in addition to the high performance of hardware resources, they also need to pay attention to the server's support capabilities in software ecology and operation and maintenance management. High-quality AI servers should support mainstream AI frameworks such as TensorFlow, PyTorch, and MXNet, and be deeply compatible with hardware acceleration libraries such as CUDA and cuDNN. At the same time, the server should also support containerized deployment, virtualization technology, job scheduling platform and monitoring system to simplify the delivery, management and expansion of AI tasks. In addition, in response to the security requirements of large-scale AI projects, servers should have multi-layer protection capabilities, including data encryption, permission segmentation, log auditing and other functions to prevent data leakage or unauthorized operations.

In summary, the configuration of AI large model servers needs to include multiple dimensions such as computing, storage, memory, network, power, and heat dissipation, and different business scenarios have different focuses. When deploying AI servers, enterprises should combine model scale, training complexity, cluster architecture and future expansion needs to select an overall solution with reasonable hardware configuration, efficient network performance, reliable data storage, and perfect security protection.

Relevant contents

How to choose a server rental provider in California, USA? CDN accelerates the core role of domain names and multi-scenario application resolution What are the coping strategies for the bandwidth resource bottleneck of enterprise servers What are the types of high-frequency CPUs for server rental in 2025? How to remotely manage Linux servers? Methods and tools recommended Main reasons and solutions for failure to connect to the gateway server General process of modifying MySQL default encoding How to solve the problem of driver loading failure or error in the server system log Several factors to pay attention to when choosing a Canadian server deployment business The main reasons and operation process of Xshell restarting the server
Go back

24/7/365 support.We work when you work

Support