Optimize Your AI, Minimize Your Costs

NVIDIA's recent acquisition of Run:ai, a startup specializing in Kubernetes-based GPU orchestration, underscores the crucial role of Kubernetes in optimizing AI workloads for the generative AI era. As enterprises ramp up their AI capabilities, seeking not only to amplify their capabilities but also to optimize costs and reduce their ecological footprints, the demand for efficient, scalable, and sustainable orchestration solutions on hybrid cloud environments has become a critical capability.

Cost Efficiency

In the realm of AI and machine learning, where computational demands are hefty, the mismanagement of resources such as GPUs can lead to substantial inefficiencies, escalating costs and energy consumption. Kubernetes addresses these challenges first by supporting a hybrid and dynamic utilization of resources across on-premises and private cloud models to balance cost and performance while benefiting from scaling up on public cloud when necessary. In fact, we have started to see a wave of repatriation of AI workloads to on-premises data centers since in the long run, buying your own GPU hardware can be more cost-effective than renting it from a public cloud provider. This is especially true if a given enterprise has a high utilization rate for its GPUs. As an example, a recent article by Ayal Steinberg from IBM shows a significant cost advantage for hosting an on-prem cluster of eight A100 GPUs vs. the continuous use of a p3.16xlarge instance with eight (less powerful) NVIDIA V100 GPUs on AWS.

Resource Optimization

When looking specifically at running AI on premises and on private cloud, the focus is therefore on maximizing the efficiency and utilization of computational resources like GPUs. in this aspect, kubernetes orchestration can help to not only ensures that hardware resources are fully utilized but also to reduce the unnecessary energy expenditure associated with idle or underutilized resources. The cost savings are complemented by a reduction in the environmental impact, aligning financial and sustainable goals in enterprise AI operations. However, at this point in time, the orchestration of complex AI workflows, especially those requiring hardware accelerators, involves intricate configuration and management that can be daunting for IT teams. This complexity is compounded when Kubernetes is deployed across diverse environments that span both private clouds and public clouds. Therefore for enterprises looking to integrate these capabilities seamlessly, leveraging an enterprise-grade Kubernetes solution with built-in infrastructure management capabilities providing optimized GPU-as-a-Service capabilities, including enhanced features such as automated management of hardware accelerators or advanced monitoring capabilities, is likely going to be a preferred strategy. At Red Hat, my team has been recently working with a number of partners including Managed Service Providers and Telco in building private GPU cloud infrastructure including GPU-accelerated near edge offerings, which provide additional cost advantages when taking into account latency optimization and data costs (particularly with AI inference).

Enhancing AI Process Scalability

Last, enterprises also seek to quickly adapt to changing business needs without excessive incremental costs. Kubernetes is the ideal foundation to standardize and simplify the complex lifecycle management of AI models, from development to deployment. By facilitating a more consistent management process across the diverse infrastructure providers in a hybrid AI infrastructure model, frameworks such as kubeflow help to make deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable, enabling enterprises to streamline the deployment of AI models, thereby shortening the time-to-market. This increased efficiency not only reduces operational costs but also allows businesses to quickly leverage new AI advancements, maintaining competitiveness while managing expenditures effectively.

Kubernetes and AI: Synergizing Scalability and Sustainability

We have seen that kubernetes excels in managing the scalability demands of any application including large-scale, complex AI operations efficiently. Its ability to manage distributed systems ensures that enterprises can scale their AI models as needed without compromising on performance. In addition, Kubernetes promotes sustainable practices by providing the possibility to optimize the use of computing resources and reducing waste, which is essential for maintaining cost-effective and environmentally friendly AI operations. However, it should be highlighted that orchestration capabilities for kubernetes in the realm of AI are not yet mature: ?the current resource management in Kubernetes has grown organically over the years and has not been optimized for AI/ML use-cases. This means there is limited ability in standard kubernetes to implement optimized scheduling features, including sophisticated job controllers and gang scheduling and to take advantage of specific inter- and intra-node topology configurations to get the best performance out of the hardware. This situation is likely to evolve rapidly in the near future, as the community looks into revisiting the Kubernetes Hardware Resource Model to adapt to the requirements of modern AI/ML workloads. Meanwhile, I have been extremely lucky to work amongst brilliant engineers across Red Hat, IBM and Intel who have been working on integrating environmental sustainability with cloud-native technology solutions: check out for example this great tutorial on Cloud Native Sustainable LLM Inference in Action at the recent Kubecon in Paris, which demonstrates how AI accelerator frequency adjustments can be leverage to optimize power-efficiency with LLM inference.

Conclusion

The integration of Kubernetes into AI strategies within hybrid cloud frameworks offers a compelling pathway to achieving enhanced operational efficiency, scalability, and sustainability. By optimizing resource usage and streamlining workflows, Kubernetes not only helps enterprises reduce their AI operational costs but also supports broader sustainability goals. As AI continues to evolve, Kubernetes will play an increasingly vital role in ensuring that enterprises can sustainably scale their AI investments, driving innovation while keeping costs in check. However, given the challenges of managing Kubernetes at scale and orchestrating complex hardware resources necessitate a more refined approach than traditional workloads. Enterprise Kubernetes solutions such as Red Hat OpenShift offer the robustness, scalability, and ease of management required to truly harness the power of AI while keeping infrastructure options open, optimizing costs and enhancing sustainability. For enterprises aiming to integrate advanced AI capabilities within their hybrid infrastructure, leveraging such enterprise Kubernetes platform is a prudent strategy that can provide operational excellence and strategic advantage without lock-in into specific infrastructure solutions.