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Quentin Anthony, AI Engineer at Eleuther AI, discusses the mathematics of training LLMs. Topics covered include compute requirements, scaling up GPUs, efficiency of back propagation, theoretical flops versus actual flops, and challenges in training language models.
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Podcast summary created with Snipd AI

Quick takeaways

  • Sharded optimizers allow for efficient memory usage and parallel training by dividing optimizer states across GPUs.
  • Different components, such as model memory, optimizer memory, activation memory, and gradient memory, play important roles in determining training memory requirements and optimizing memory usage.

Deep dives

Sharded Optimizers and 3D Parallelism

Sharded optimizers involve dividing the optimizer states across GPUs, enabling parallel processing. It works through scatter and gather operations to distribute and combine the optimizer states and gradients on each GPU. This approach allows for efficient memory usage and parallel training. 3D parallelism combines data parallelism, tensor parallelism, and pipeline parallelism. Data parallelism distributes data across GPUs, tensor parallelism splits the model along tensors across GPUs, and pipeline parallelism splits the model along layers across GPUs. Each approach has its own benefits and trade-offs, providing different levels of control and scalability. The choice of parallelism depends on factors such as interconnect speed, GPU memory capacity, and the size of the model being trained.

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