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GPUs + Kubernetes =? Decoding next-gen AI-enabling workloads
Latest   Machine Learning

GPUs + Kubernetes =? Decoding next-gen AI-enabling workloads

Last Updated on March 13, 2024 by Editorial Team

Author(s): Mélony Qin (aka cloudmelon)

Originally published on Towards AI.

Cloud-native technologies such as Kubernetes and serverless have been revolutionizing modern application design and deployment in recent years. Now, with AI's rising importance, Kubernetes and GPUs are becoming the backbone of some major AI research companies. For instance, OpenAI is leveraging them to train its complex multimodal AI model scaling to 7,500 worker nodes. So, in this blog, let’s talk about some interesting facts about Kubernetes and GPUs for next-gen AI-enabling workloads on large-scale GPU-accelerated computing together.

GPUs + Kubernetes = ?

In 2024, Kubernetes continues to witness widespread adoption, serving as the backbone for organizations seeking to streamline the deployment, management, and scaling of containerized applications. This surge in adoption prompts DevOps, platform engineering, and development teams to prioritize the reliability, security, and cost efficiency of their workloads, according to the Kubernetes Benchmark Report 2024 based on their analysis data from over 330,000 workloads.

Kubernetes isn’t just a platform but an entire ecosystem. Since CNCF started in 2015, it’s been home to lots of important projects, like Kubernetes, Prometheus, Envoy, and others. Right now, CNCF hosts 173 projects, with over 220,000 people from 190 countries helping out. This shows how big and diverse the Kubernetes community is, and how it’s not just… Read the full blog for free on Medium.

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Published via Towards AI

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