This post was originally published on Info World
Generative AI has emerged as the focus of this year’s KubeCon + CloudNativeCon. In most cloud computing conferences this year, GenAI has dominated keynote presentations and shed light on the evolution of cloud-native platforms.
Most companies who took the stage at KubeCon announced that they already leverage cloud-native platforms to support generative AI applications and large language models (LLMs). The tone was “us too!” more than a solid explanation of the strategic advantages of their approach. You can sense an air of desperation, especially from companies that poo-pooed cloud computing in the beginning but had to play catch-up later. They don’t want to do that again.
What’s new in the cloud-native world?
First, cloud-native architectures are essential to cloud computing, but not because they provide quick value. It’s a path to building and deploying applications that function in optimized ways on cloud platforms. Cloud-native uses containers and container orchestration as the base platforms but has a variety of features that are part standard and non-standard components.
What needs to change in cloud-native to support generative AI? Unique challenges exist with generative AI, as was pointed out at the event. Unlike traditional AI model training where smaller GPUs may suffice for inference, LLMs require high-powered
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