Project Description
- Scope of activity: AI development, deep learning and high performance computing (HPC).
- Software used: Frameworks PyTorch, TensorFlow, Docker containers for neural network computing, data processing libraries.
Key requirement: Uncompromising GPU processing power and the fastest storage subsystem. An important condition was high bandwidth between graphics cards for parallel computing.
Selection process and decision
Standard server solutions were not suitable for implementing such a task. We chose the ASUS ESC8000A-E13P 4U platform, which is the standard for GPU-oriented systems.
- Graphics power: The choice fell on the latest NVIDIA H200 accelerators with a memory capacity of 141 GB each. This is the “gold standard” for working with neural networks in 2026. To combine them into a single computing ecosystem, we used NVLink bridges, which provide data exchange at speeds not available on the conventional PCIe bus.
- Computing center: Two AMD EPYC 9374F processors provide 64 high-frequency cores, which is critical for data preparation (preprocessing) before it is sent to the GPU.
- Memory and storage: We equipped the system with 1.5 TB of RAM and advanced Samsung PM9D3a NVMe drives with PCIe 5.0 support. A read speed of 12,000 MB/s ensures that there are no bottlenecks when working with huge datasets.
Network interface: To integrate the server into the customer's existing infrastructure, a dual-port Mellanox ConnectX-6 adapter is installed, supporting speeds of up to 25 Gbit/s.
Result
Our company's engineers assembled and tested one of the most powerful servers in its class.
- Performance: The use of NVLink bridges and the H200 architecture has reduced the training time for specific customer models by several times compared to the previous generation of systems.
- Reliability: The server has passed 48-hour stress testing. Thanks to the ASUS server platform with redundant cooling and power supplies, the system is ready for operation 24/7/365.
Scalability: The configuration leaves the possibility of adding 4 more NVIDIA H200 accelerators without replacing the basic components of the system.
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