6 key considerations for selecting an AI systems vendor

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6 key considerations for selecting an AI systems vendor

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Many IT leaders are responding to C-suite pressure for artificial intelligence (AI) capabilities by increasing the organization’s AI investment in 2024. They’re finding that AI success is a complex task, and they need all they help they can get.

Choosing the right AI systems vendor – one with the right capabilities – won’t solve all these issues. But it can simplify achieving your AI goals.

IT leaders attending NVIDIA’s GTC 2024 AI developer conference on March 18-21, 2024, in San Jose, CA, can explore these capabilities with ASUS, one of the global leaders in high-performance AI servers based on NVIDIA’s MGX server reference architecture. ASUS servers exploit the latest NVIDIA advances in GPUs, CPUs, NVME storage, and PCIe Gen5 interfaces.

To achieve AI success, IT leaders need a systems vendor who goes far beyond simply plugging the latest GPU processor into a standard rack-mount server. Here are six critical capabilities that IT leaders should look for when choosing an AI systems vendor for datacenter deployment.

1. Maximize performance

Look for servers equipped with the most advanced CPUs or GPUs optimized for AI workloads. Asses the processors to ensure they meet the compute demands of your AI algorithms.

  • ASUS mid- and high-range NVIDIA MGX servers incorporate the latest NVIDIA chip offerings, including the Grace Hopper Superchip, the GH200 Grace CPU Superchip and the full NVIDIA GPU portfolio.

2. Maximize data storage

AI workloads demand vast amounts of data. Evaluate vendors’ storage options, including SSDs, HDDs, and especially NVMe drives. Consider capacity, speed, and scalability.

  • ASUS AI servers leverage an array of high performance storage options, including: SupremeRAID, from Graid Technology, which is RAID software loaded on a GPU card to maximize storage performance without eating up CPU cycles; and support for NVIDIA PCI Express Gen 3, 4, and 5 interfaces.

3. Consider scalability and flexibility

Look for AI server solutions that can demonstrably scale both horizontally and vertically to accommodate growing AI workloads. Asses modular designs, expansion options, and support for cloud-based or hybrid deployments.

  • ASUS can tailor its NVIDIA MGX-based servers to target an organization’s initial AI pilot and quickly add compute and storage capacity as needed.

4. Understand cooling options

AI compute servers generate vast amounts of heat compared to conventional servers. Evaluate the vendor’s ability to minimize server heat output cost-effectively and reliably.

  • ASUS has an array of cooling options, from efficient air systems, through direct-to-chip liquid cooling to full immersion cooling.

5. Ask about AI framework support

Document which popular AI frameworks – such as TensorFlow, PyTorch, or MXNet – the vendor supports. Compatibility with these frameworks can streamline development and deployment workflows.

  • Besides supporting the NVIDA AI frameworks, ASUS has created its own no-code AI platform to speed an organization’s AI development on large language model (LLM) pre-training, fine-tuning, and inference.

6. Check into reliability and support

Assess the vendor’s track record for reliability, quality, and customer support. Look for vendors with a reputation for delivering robust, enterprise-grade server solutions and responsive technical support services.

  • ASUS servers power some of the most demanding high-performance computing environments, including Taiwan Web Service (TWSC), an ASUS subsidiary. The company has 2,000 support centers in 200 countries.

To learn more, visit https://servers.asus.com/



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