Choose from a range of workload-specific validated configurations for GPU-accelerated servers and workstations.
Overview
The article discusses the optimization of enterprise IT workloads using NVIDIA-Certified Systems, addressing the challenges of selecting appropriate hardware for GPU-accelerated applications. It highlights the certification process that ensures systems meet performance, manageability, security, and scalability requirements.
What You'll Learn
1
How to select NVIDIA-Certified Systems for optimized performance in enterprise IT workloads
2
Why proper server configuration is critical for GPU performance
3
When to utilize specific NVIDIA GPUs for different workloads
Key Questions Answered
What is the NVIDIA-Certified Systems program?
The NVIDIA-Certified Systems program ensures that hardware systems meet specific performance and scalability criteria for GPU-accelerated applications. It involves rigorous testing by system manufacturers and NVIDIA to validate management and security capabilities, ultimately providing reliable configurations for enterprise IT workloads.
How does the certification process improve server performance?
The certification process involves running over 25 software tests that simulate real-world applications, addressing issues like high operating temperatures, non-optimal BIOS settings, and improper PCI configurations. This thorough testing helps identify and rectify configuration problems that could hinder performance, ensuring optimal operation of the systems.
What are the key categories of NVIDIA-Certified Systems?
NVIDIA-Certified Systems are categorized based on their intended workloads, including Data Center Compute Servers for AI training, High Density Virtualization Servers for remote work, and Industrial Edge systems for rugged environments. Each category is tailored for specific use cases, ensuring optimal performance and reliability.
What distinguishes qualification from certification in NVIDIA systems?
Qualification involves thermal, mechanical, and power tests to ensure a GPU functions in a server design, while certification includes additional performance and functionality tests. Certified systems are both supported for production use and optimized for performance, making them preferable for enterprise applications.
Technologies & Tools
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Hardware
Nvidia Gpus
Used for accelerating workloads in various applications, including AI and data analytics.
Orchestration
Kubernetes
Used for managing certified servers in cloud-native environments.
Management
Redfish
Provides remote management capabilities for certified systems.
Key Actionable Insights
1Choose NVIDIA-Certified Systems to ensure optimal performance for GPU-accelerated workloads.These systems undergo rigorous testing to meet performance and security standards, making them a reliable choice for enterprise IT.
2Pay attention to server configuration details, such as BIOS settings and PCI slot placement.Improper configurations can lead to significant performance issues, so following best practices during setup is crucial.
3Utilize the NVIDIA certification process to validate system manageability with cloud-native frameworks.This ensures that your systems can be effectively managed and scaled using tools like Kubernetes and Red Hat OpenShift.
Common Pitfalls
1
Neglecting proper server cooling configurations can lead to overheating and performance degradation.
Many default fan curves do not account for the heat generated by GPUs, so custom configurations may be necessary to maintain optimal temperatures.
2
Using non-optimal BIOS and firmware settings can negatively impact system performance.
It's essential to validate and adjust these settings during the certification process to ensure the best possible performance.
Related Concepts
Gpu-accelerated Workloads
Nvidia-certified Systems
Enterprise It Infrastructure