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Deployment Challenges of Large Models
An AI development team has successfully created a large, state-of-the-art language model on a high-performance computing cluster. The model shows excellent accuracy on its target task. However, when they try to deploy this model as part of a real-time chatbot application for consumer-grade laptops, they encounter significant issues. Analyze the fundamental conflict between the model's characteristics and the requirements of the deployment environment, explaining at least two specific problems that are likely to arise for the end-user.
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Data Science
Foundations of Large Language Models Course
Computing Sciences
Analysis in Bloom's Taxonomy
Cognitive Psychology
Psychology
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Empirical Science
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Deployment Challenges of Large Models
For any real-world application, applying compression techniques to a large pre-trained model is the optimal deployment strategy because it reduces model size and improves computation efficiency without compromising the model's performance.