AI training efficiency: From Throughput to Goodput
AI Summary1 min read
TL;DR
Training large AI models requires massive resources and time. Success is measured by data processing speed and learning progress, not just raw throughput.
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Pretraining a modern large language model (LLM), often with ~100B parameters or more, typically involves thousands of accelerators and massive token corpora, running for days to months. At that scale, success is commonly reduced to two headline outcomes: Speed: how fast the system consumes training data, usually measured in tokens/second. Learning: how much progress is […]
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