The H3C UniServer R5300 G3 is an artificial intelligence server. It is a new-generation GPU server developed by Xinhua San Group for artificial intelligence and high-performance computing. It can accelerate the construction of deep learning models, analyze data with great speed, and derive valuable information. Utilize powerful computing power to predict future activities, behaviors and trends, and use big data analysis to discover and solve problems hidden in the data.
- H3C UniServer R5300 G3 supports 8 double-width GPUs or 20 single-width GPUs, providing stronger computing capabilities.
- H3C UniServer R5300 G3 is designed for CPU / GPU heterogeneous computing and adopts PCIe4.0 communication link design, which can realize high-speed and low-latency data communication between GPUs, bringing users an better performance experience.
- The host is equipped with a high-speed and low-latency network adapter, NVMe driver, and the latest APE memory is adapted to bring powerful performance.
Flexible support for deep learning and HPC workloads
- The H3C UniServer R5300 G3 server supports a variety of mainstream operating systems and virtualization environments, including Microsoft® Windows® and Linux operating systems, and environments such as H3C CAS. It flexibly adapts to a variety of business needs.
- Support existing data center infrastructure standards and accelerate deep learning and HPC applications on the existing foundation.
- Enterprise-class reliability, availability, and maintainability reduce total cost of ownership
Easier to use and upgrade with an easily accessible modular design
- N + N redundant power supplies and N + 1 hot-swappable redundant fans ensure safe and stable system operation. Optimized thermal design reduces system power consumption and saves operating costs.
- The server integrates a management and monitoring chip and provides powerful centralized management software, which can easily implement remote management and status monitoring of the server, reducing the workload of maintenance personnel.