The Vultr team has been exploring trends to watch in 2024, examining the world of GenAI and Large Language Models. In this dynamic landscape of technology, artificial intelligence (AI) and machine learning (ML) also continue to evolve, pushing the boundaries of innovation. As we enter 2024, a notable transformation is on the horizon – the full cloud-native integration of AI and ML. The extension of CPU-based Kubernetes clusters marks this shift to accommodate containerized services and models running on GPU-based clusters. Cloud engineering and architecture principles are seamlessly transferring to the GPU world, providing developers with a powerful platform for their development endeavors.
The rise of GPU-based clusters
Traditionally, AI and ML models found their execution ground on CPU-based systems. However, the demand for accelerated processing power has paved the way for the integration of Graphics Processing Units (GPUs) into cloud-native development. According to Gartner, "By 2025, 80% of customer service and support organizations will be applying generative AI technology in some form to improve agent productivity and customer experience (CX)." Anticipate a significant surge in the use of GPU-based clusters in 2024, leveraging their parallel processing capabilities to enhance the speed and efficiency of AI and ML workloads.
Extending Kubernetes to GPUs
Kubernetes, the open-source container orchestration platform, has been a game-changer for cloud-native application development. In the coming year, we will witness the extension of Kubernetes clusters to incorporate GPU resources seamlessly. This development is crucial for developers familiar with Kubernetes and container registries for application development on CPUs. The transition to GPU-based clusters ensures a smooth and efficient shift of their development efforts, empowering them to harness the full potential of accelerated computing.
Containerized services and models on GPUs
Containerization has become a standard practice in modern software development, providing scalability, portability, and resource efficiency. According to Gartner, "Digital customer service will transform customer experience outcomes by reducing friction and eliminating unnecessary customer effort." In 2024, optimize containerized services and models for GPU-based clusters, enhancing performance and allowing developers to leverage parallel processing benefits. This optimization unlocks new possibilities for handling complex AI and ML tasks.
Empowering developers with cloud engineering principles
The integration of GPU-based clusters into the cloud-native ecosystem brings a transfer of cloud engineering principles to the GPU world. Developers who have mastered the art of cloud-native development on CPUs will find themselves equipped with the knowledge and tools needed to navigate the GPU landscape seamlessly. This empowerment opens up avenues for innovation and accelerates the pace of AI and ML advancements.
Challenges and considerations
While the shift to full cloud-native AI and ML development on GPUs brings numerous benefits, it also presents challenges. Developers must consider resource allocation, scalability, and optimization factors for different GPU architectures. Additionally, ensuring compatibility with existing Kubernetes workflows and tools is paramount for a smooth transition.
As we delve further into 2024, the full cloud-native integration of AI and ML on GPU-based clusters represents a pivotal technological moment. The extension of Kubernetes, optimization of containerized services, and the transfer of cloud engineering principles to the GPU world collectively usher in a new era of possibilities. Developers are poised to unlock unprecedented potential, driving innovation and shaping the future of artificial intelligence and machine learning. The cloud-native revolution continues, and its impact on AI and ML development is nothing short of transformative.
Want to see more of what’s coming in 2024? See our predictions here, and keep an eye out for more posts in this series!
