In the dynamic landscape of modern technology, artificial intelligence (AI) has emerged as a transformative force. The technology is revolutionising industries and creating an unprecedented demand for high performance computing solutions. As a result, AI applications are becoming increasingly sophisticated and pervasive across sectors such as finance, healthcare, manufacturing, and more. In response, data centre providers are encountering unique challenges in adapting their infrastructure to support these demanding workloads.
AI workloads are characterised by intensive computational processes that generate substantial heat. This can pose significant cooling challenges for data centres. Efficient and effective cooling solutions are essential to facilitate optimal performance, reliability and longevity of IT systems.
The importance of cooling for AI workloads
Traditional air-cooled systems, commonly employed in data centres, may struggle to effectively dissipate the heat density associated with AI workloads. As AI applications continue to evolve and push the boundaries of computational capabilities, innovative liquid cooling technologies are becoming indispensable. Liquid cooling methods, such as immersion cooling and direct-to-chip cooling, offer efficient heat dissipation directly from critical components. Thishelps mitigate the risk of performance degradation and hardware failures associated with overheating.
Deploying robust cooling infrastructure tailored to the unique demands of AI workloads is imperative for data centre providers seeking to deliver high-performance computing services efficiently, reliably and sustainably.
Advanced cooling technologies for AI
Flexibility is key when it comes to cooling. There is no “one size fits all” solution to this challenge. Data centre providers should be designing facilities to accommodate multiple types of cooling technologies within the same environment.
Liquid cooling has emerged as the preeminent solution for addressing the thermal management challenges posed by AI workloads. However, it’s important to understand that air cooling systems will still be part of data centre’s for the foreseeable future.
Immersion Cooling
Immersion cooling involves submerging specially designed IT hardware (servers and graphics processing units, GPUs) in a dielectric fluid. These fluids tend to comrpise mineral oil or synthetic coolant. The fluid absorbs heat directly from the components, providing efficient and direct cooling without the need for traditional air-cooled systems. This method significantly enhances energy efficiency. As a result, it also reduces running costs, making it ideal for AI workloads that produce substantial heat.
Immersion cooling facilitates higher density configurations within data centres, optimising space utilisation and energy consumption. By immersing hardware in coolant, data centres can effectively manage the thermal challenges posed by AI applications.
Direct-to-Chip Cooling
Direct-to-chip cooling, also known as microfluidic cooling, delivers coolant directly to the heat-generating components of servers, such as central processing units (CPUs) and GPUs. This targeted approach maximises thermal conductivity, efficiently dissipating heat at the source and improving overall performance and reliability.
By directly cooling critical components, the direct-to-chip method helps to ensure that AI applications operate optimally, minimising the risk of thermal throttling and hardware failures. This technology is essential for data centres managing high-density AI workloads.
Benefits of a mix-and-match approach
The versatility and flexibility of liquid cooling technologies provides data centre operators with the option of adopting a mix-and-match approach tailored to their specific infrastructure and AI workload requirements. Integrating multiple cooling solutions enables providers to:
- Optimise Cooling Efficiency: Each cooling technology has unique strengths and limitations. Different types of liquid cooling can be deployed in the same data centre, or even the same hall. By combining immersion cooling, direct-to-chip cooling and / or air cooling, providers can leverage the benefits of each method to achieve optimal cooling efficiency across different components and workload types.
- Address Varied Cooling Needs: AI workloads often consist of diverse hardware configurations with varying heat dissipation characteristics. A mix-and-match approach allows providers to customise cooling solutions based on specific workload demands, ensuring comprehensive heat management and system stability.
- Enhance Scalability and Adaptability: As AI workloads evolve and data centre requirements change, a flexible cooling infrastructure that supports scalability and adaptability becomes essential. Integrating multiple cooling technologies provides scalability options and facilitates future upgrades without compromising cooling performance. For example, air cooling can support HPC and AI workloads to a degree, and most AI deployments will continue to require supplementary air cooled systems for networking infrastructure. All cooling types ultimately require waste heat to be removed or re-used, so it is important that the main heat rejection system (such as chillers) is sized appropriately and enabled for heat reuse where possible.
A cooler future
Effective cooling solutions are paramount if data centres are to meet the ever-growing demands of AI workloads. Liquid cooling technologies play a pivotal role in enhancing performance, increasing energy efficiency and improving the reliability of AI-centric operations.
The adoption of advanced liquid cooling technologies not only optimises heat management and reuse but also contributes to reducing environmental impact by enhancing energy efficiency and enabling the integration of renewable energy sources into data centre operations.
- Data & AI
- Infrastructure & Cloud
