Businesses that deploy generative AI as part of enterprise software quickly discover that, while large language models (LLMs) such as OpenAI’s GPT-3 and its successors demonstrate impressive capabilities, they are economically difficult to use in real-world applications. This is largely due to the high demand for compute resources, not only when training but also in inference — particularly when the application’s quality of service calls for fast responses.

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