When designing AI infrastructure, businesses face critical decisions that can significantly impact performance, control, and cost efficiency. Choosing the right architecture is essential. NVIDIA DGX offers a turnkey AI platform with guaranteed performance, reliability, and a single point of support, making it ideal for streamlined deployments. On the other hand, HGX provides a flexible, customizable solution with OEM components, perfect for organizations that need tailored architectures and more control over their setup.
Another key consideration is whether to opt for cloud-based or the control of an owned AI stack. Cloud solutions are scalable and accessible but can bring unpredictable costs, compliance, and performance limitations. In contrast, an owned AI stack ensures full control, data autonomy, predictable costs, often a lower CTO, and guaranteed performance, making it a more robust option for long-term investments.
The location of your infrastructure also matters. On-premises solutions, managed internally within your facilities, provide maximum control and seamless integration. Alternatively, colocation in certified data centers offers shared spaces that meet specific standards while maintaining flexibility. For businesses seeking rapid deployment and scalability, modular data centers provide an ideal solution. These prefabricated, energy-efficient facilities are designed for high-performance AI workloads and can be quickly deployed with advanced cooling systems, ensuring flexibility, sustainability, and cost-effectiveness.
Management and support decisions are closely tied to your in-house competency and capacity, directly shaping operational efficiency. Self-managed setups offer full control and independence but require a skilled team and sufficient internal resources. In contrast, MDCS.AI-managed solutions reducing the demand on internal capacity while ensuring smooth operations and access to managed services. Your choice should reflect the availability of in-house expertise and your organization’s ability to manage complex AI infrastructure effectively.
Finally, financial planning plays a crucial role. Opting for an Opex model allows lifecycle management with automatic updates to the latest technology, offering scalability and flexibility. A Capex model, on the other hand, involves a one-time investment that maximizes the potential and lifespan of your technology.
Each of these factors contributes to the success of your AI strategy. Weigh your options carefully to align your choices with your business goals and resources.
