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Distributed Training, Decentralized AI: Prime Intellect's Master Plan to Make AI Too Cheap to Meter

"The Cognitive Revolution" | AI Builders, Researchers, and Live Player Analysis

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Advancements in Distributed Training for AI

This chapter explores the concept of swarm parallelism and its role in improving model training efficiency in distributed systems. The discussion covers challenges such as latency, memory bottlenecks, and the importance of fault tolerance in training large AI models across numerous GPUs. Additionally, it highlights the evolution of hardware, open-source contributions, and the implications of decentralized training on the future of AI.

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