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Ep 74: Chief Scientist of Together.AI Tri Dao On The End of Nvidia's Dominance, Why Inference Costs Fell & The Next 10X in Speed

Unsupervised Learning

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Optimizing AI Inference for the Future

This chapter examines the complexities of resource allocation in AI inference at scale, focusing on the benefits of batch processing for cost and efficiency improvements. It predicts a shift toward advanced AI workloads requiring innovative optimizations that cater to industry-specific demands, particularly in fields such as engineering and finance. Furthermore, it emphasizes the need for architectural innovations and collaboration within the AI community to enhance inference performance and reduce expenses, paving the way toward achieving Artificial General Intelligence.

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