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Beyond Nvidia: Why tech firms are turning to custom chips for AI inference

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Beyond Nvidia: Why tech firms are turning to custom chips for AI inference

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Beyond Nvidia: Why tech firms are turning to custom chips for AI inference

Microsoft's Maia 300 joins custom AI chips from Google, Amazon and OpenAI as technology companies seek to reduce inference costs, improve efficiency and gain control over infrastructure

Technology companies, including Microsoft, Google and Amazon, are developing custom AI chips to cut inference costs, improve power efficiency and reduce reliance on external processors.

Sweta Kumari New Delhi 7 min read Last Updated : Oct 13 2026 | 5:06 PM IST

Microsoft has announced Maia 300, its next-generation custom artificial intelligence (AI) chip designed to run AI models across its data centres, as technology companies seek to control the cost of operating AI services. The company plans to begin large-scale deployments of its Azure Maia systems in 2027, with the chips expected to become the primary inference platform for its products and services.

Microsoft is not the only major technology company deploying custom chips for AI inference. From Google's Tensor Processing Units (TPUs) to Amazon's Trainium and OpenAI's planned inference chip, the industry is moving towards greater control over the hardware that powers AI services.

AI computing has two broad stages: training and inference. Training involves processing large amounts of data to build a model, while inference is the process of using that trained model to generate an answer, write code, summarise a document or complete another task.

As AI tools become available to more users, inference becomes a recurring operating expense.


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