It’s no secret that every organization is in the race to leverage AI – and as a result, acquiring AI infrastructure as quickly as possible. On this journey, decision-makers need to stay focused on the transformation opportunity, mapping use cases, and optimizing infrastructure needs to maximize ROI.
In a previous blogpost, we talked about AI deployments that are like Formula 1 cars: they offer unprecedented performance but require dedicated specialized operations teams and continuous maintenance, all of which costs a fortune. Most organizations don’t need F1 cars. They need practical vehicles – and AI deployments – that can run smoothly, run with little to no maintenance, and most importantly, be adaptable to specific needs & use cases.
The ability to adapt is critical to AI deployments, as it enables organizations to meet the needs of use cases today and scale up capabilities as demand grows in the future. This adaptability, however, is only possible if decision-makers play their cards right and factor various future risks into their AI infrastructure strategy today.
Embracing Open Standards and Scalability
When it comes to technology, the key to de-risking AI infrastructure lies in embracing open standards and ensuring scalability. The AI accelerator ecosystem is rapidly evolving, with significant innovation occurring across various hardware components, such as GPUs, CPUs, and specialized AI hardware from various cloud providers and niche semiconductor companies.
Within reason, organizations must avoid locking themselves into proprietary technologies, which can limit flexibility and increase costs over time.
For example, using open technologies like Ethernet for interconnecting GPUs instead of a proprietary network allows businesses to adapt to new advancements and integrate diverse hardware solutions without extensive overhauls.
A great example of this is Cisco’s Nexus 9000 which recently went through a meticulous and thorough process to acquire the certification to support Intel’s Gaudi 2 accelerator architecture. Cisco’s team built out lab environments with Gaudi 2 servers, each equipped with multiple Gaudi 2 accelerators, interconnected through a Nexus 9000 Ethernet fabric.
Extensive performance testing and validation were conducted across this setup to ensure seamless operation, performance and compatibility. The team documented all configurations, including buffer thresholds, queue depths, and QoS policies, to provide customers with a comprehensive guide for deploying similar environments. This rigorous process guarantees that the infrastructure is not only high-performing but also reliable and ready for enterprise use, significantly de-risking the deployment for customers.
Overall, such validation ensures that enterprises can leverage high-performance Intel accelerators while maintaining an open, scalable network infrastructure. By opting for open standards, businesses can mitigate risks associated with rapid technological changes and avoid being tied to a single-vendor ecosystem.
Right-Sizing and Total Cost of Ownership
Investing in AI infrastructure involves more than just acquiring cutting-edge technology. It requires a strategic approach to ensure the infrastructure is tailored to the organization’s specific workloads and long-term objectives.
Businesses must consider the total cost of ownership (TCO) beyond the initial capital expenditure (CapEx). This encompasses ongoing operational expenses such as power consumption, data center space, cooling solutions, and the specialized skills required to manage the new infrastructure.
For instance, deploying advanced GPUs may necessitate additional investments in high-efficiency cooling systems, such as liquid cooling, and specialized personnel trained in managing these systems.
Right-sizing AI infrastructure involves selecting hardware that is precisely matched to the specific AI workloads. For example, workloads focused on inferencing or fine-tuning models may not require the most powerful GPUs but rather those optimized for these tasks. Utilizing hardware that aligns with the specific computational requirements ensures that resources are not over-provisioned, which can lead to significant unnecessary costs.
Moreover, organizations should perform detailed capacity planning and workload analysis to forecast future demands. This includes evaluating the scalability of the infrastructure to accommodate growing data and increasing computational requirements.
Implementing modular and scalable solutions, such as Cisco’s UCS X-Series with integrated GPUs, allows for incremental upgrades and expansions, ensuring that the infrastructure evolves in tandem with the organization’s AI ambitions.
By focusing on TCO and right-sizing, businesses can optimize their investments, ensuring that their AI infrastructure is both cost-effective and capable of supporting their long-term goals. This strategic alignment not only maximizes ROI but also minimizes the risks associated with underutilized or overextended resources.
Diversifying Suppliers and Mitigating Risk
Supplier diversification is a critical strategy in de-risking AI infrastructure.
Dependence on a single vendor for all components can expose organizations to significant risks, including supply chain disruptions, price volatility, and limitations in scalability and support.
To mitigate these risks, it is essential to adopt an infrastructure platform that enables the organization to leverage multiple vendors as needed. Cisco’s collaboration with the Ultra Ethernet Consortium, which includes industry leaders such as AMD, Intel, Microsoft and Meta, exemplifies the benefits of a diversified supplier strategy. This consortium focuses on advancing Ethernet technology for AI and machine learning workloads, ensuring that businesses have access to a robust and interoperable network infrastructure.
By leveraging multiple suppliers, organizations can benefit from a broader range of innovations and maintain flexibility in their AI infrastructure. Integrating GPUs from different manufacturers allows businesses to select the best-performing hardware for specific workloads, optimizing performance and cost-efficiency.
This approach also provides a safeguard against supply chain disruptions, as reliance on a single supplier is minimized. Adopting open standards, such as Ethernet for GPU interconnects, further enhances the flexibility and scalability of AI infrastructure.
Unlike proprietary solutions, open standards enable seamless integration of new technologies and hardware components as they become available, allowing businesses to stay at the forefront of innovation. This approach not only reduces the risk of vendor lock-in but also ensures that the infrastructure can evolve to meet future demands.
De-risking is Critical to Your AI Strategy
De-risking AI infrastructure requires a thoughtful and strategic approach that balances technology choices, investment considerations, and supplier diversification.
By embracing open standards, focusing on total cost of ownership, and diversifying suppliers, organizations can build resilient, scalable AI infrastructures that are well-prepared for the future. As the AI landscape continues to evolve, businesses must stay agile and adaptable, ensuring that their infrastructure investments today can meet the demands of tomorrow. Learn more about Cisco’s AI solutions online here.
