How do distributed training systems scale frontier LLMs across thousands of GPUs?

Distributed training scales LLM development by partitioning computation, parameters, and data across massive GPU clusters.

Frontier LLMs often contain hundreds of billions or trillions of parameters, making single-device training impossible.

Distributed training systems therefore split workloads across many GPUs using parallelism techniques.

Key approaches include:

Different GPUs process different mini-batches while synchronizing gradients.

Individual tensor operations are split across GPUs.

Different model layers run on different devices.

Sparse MoE experts distributed across hardware.

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