2 GPU - Single server - Windows Server 2019 compatible - Blade servers

Blade servers offer a compact yet powerful computing solution, designed to maximize processing power while minimizing the footprint in rack space. This modern architecture integrates server, networking, and storage capabilities with unified remote management features. Our blade servers are perfect for a variety of demanding applications.

Key Advantages:

• Space Efficiency: Reduces the need for excessive rack space, simplifying cabling and maintenance.

• Enhanced Power Management: Optimizes power consumption, crucial for maintaining efficiency in large-scale operations.

• Unified Remote Management: Allows for seamless management of server health and resources from a remote location.

• Versatile Applications: Ideal for cloud computing, video streaming, content delivery, social networking, desktop virtualization, and remote workstations.

Discover our comprehensive selection of multi-processor servers to find the ideal solution tailored to your specific needs.

Servers using WS2K19, also known as Windows Server 2019, are widely utilized in enterprise data centers, private clouds, and remote offices for applications like server virtualization, Active Directory, and container orchestration. The server OS offers advantages such as robust security features, improved scalability, and enhanced support for hybrid cloud scenarios.
A single-node server offers a straightforward, easy-to-manage solution ideal for small businesses or individual departments within a larger organization. These servers are commonly used for lightweight applications, file storage, and simpler database management, providing cost-effective performance and minimal setup complexity.
Servers with the capacity for two GPUs offer a balanced combination of performance and scalability, making them ideal for medium to large enterprise applications that require advanced data analytics or complex simulations. The dual-GPU configuration provides not only redundancy but also the ability to significantly speed up parallel computing tasks, enhancing overall system efficiency.