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Are We Ready for Large-scale AI Workloads?
Originally published in Embedded
ChatGPT has fired the world’s imagination about AI. The chatbot can write essays, compose music, and even converse in different languages. If you’ve read any ChatGPT poetry, you can see it doesn’t pass the Turing Test yet, but it’s a huge leap forward from what even experts expected from AI just three months ago. Over one million people became users in the first five days, shattering records for technology adoption.
The groundswell also strengthens arguments that AI will have an outsized impact on how we live—with some predicting AI will contribute significantly to global GDP by 2030 by fine-tuning manufacturing, retail, healthcare, financial systems, security, and other daily processes.
But the sudden success also shines light on AI’s most urgent problem: our computing infrastructure isn’t built to handle the workloads AI will throw at it. The size of AI networks grew by 10x per year over the last 5 years. By 2027 one in five Ethernet switch ports in data centers will be dedicated to AI, ML and accelerated computing.
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