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Businesses are increasingly focused on scaling AI but face challenges like high infrastructure costs and computational demands. Large language models (LLMs) are often prioritized for their size and accuracy, yet student models present a promising alternative. These models can help companies transition from large, centralized systems to more efficient edge devices, enabling real-time applications.
Student models utilize knowledge distillation to transfer learnings from complex teacher models to smaller, more efficient models. This process not only reduces computational requirements but also maintains high performance, making these models suitable for critical applications like self-driving cars. The article emphasizes the importance of ethical considerations and compliance in developing these models to ensure fairness and privacy.
• Knowledge distillation enables efficient AI model deployment on edge devices.
• Student models provide faster response times and improved privacy for users.
This technique allows for the creation of efficient models that retain high performance while reducing computational demands.
Student models are designed to be efficient and effective for deployment in resource-constrained environments.
This approach enhances speed and privacy, making it ideal for applications requiring real-time decision-making.
Fujitsu's focus on AI democratization aligns with the article's emphasis on scaling AI for broader accessibility.
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