Specialized Hardware for AI: Rethinking Assumptions and Implications for the Future - Gradient Flow

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Top Highlights

• specialized AI hardware for data centers

• assumptions

• expectations

• training serves as a control point

• streamlining inference

• improve software performance without sacrificing accuracy.

• same hardware for training and inference,

• Models continue to get larger

• simple and flexible, distributed computing frameworks.

• excitement surrounding Generative AI and Foundation Models

• focus on data tasks

• decentralized custom models

• create entirely new content,

• analyzing

• responding

• foundation models

• trained on broad data and then adapted for downstream tasks and applications.

• language model (large models that take in text and output text).

• tailor them to their specific needs and use cases,

• create their own