Optimizing AI Training: Beyond Simple Throughput

Pretraining modern large language models (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 made.

For those evaluating on-premise deployments, there are trade-offs to consider. AI-RADAR offers analytical frameworks on /llm-onpremise to evaluate these aspects.

Assessing training efficiency requires a broader view than just throughput. It is essential to also consider "goodput", which is the amount of useful work actually done by the AI system. This implies optimizing not only the processing speed, but also the quality of the results obtained during training.