26min chapter

Latent Space: The AI Engineer Podcast — Practitioners talking LLMs, CodeGen, Agents, Multimodality, AI UX, GPU Infra and all things Software 3.0 cover image

The Mathematics of Training LLMs — with Quentin Anthony of Eleuther AI

Latent Space: The AI Engineer Podcast — Practitioners talking LLMs, CodeGen, Agents, Multimodality, AI UX, GPU Infra and all things Software 3.0

CHAPTER

Theoretical Flops vs Actual Flops in Training Language Models

This chapter explores the concept of theoretical flops versus actual flops in training language models. It discusses the differences between theoretical and actual flops, including the idle time during training and the comparison of expected flops for different GPUs. The chapter also highlights the benefits of the NVIDIA and PyTorch stack in terms of developer support and issue resolution.

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