Learn how to estimate memory requirements for your LLM fine-tuning experiments using Red Hat Training Hub's memory_estimator.py API. This guide covers the memory components, adjusting training setups for specific GPU specifications, and using the memory estimator in your code. Streamline your model fine-tuning process with runtime estimates and automated hyperparameter suggestions.

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Fine-tuning LLMs requires significantly more GPU memory than inference, and launching experiments without planning can waste GPU hours. Red Hat AI's Training Hub (starting with OpenShift AI 3.0) includes a `memory_estimator.py` API to estimate VRAM requirements before running experiments. The post explains the memory components involved (model, gradient, optimizer, activation, output), covers how SFT, LoRA, QLoRA, and OSFT differ in memory usage, and provides strategies to reduce memory consumption. It also shows how to use the estimator classes (`BasicEstimator`, `LoRAEstimator`, `QLoRAEstimator`, `OSFTEstimator`) in Python notebooks, with example output showing per-GPU memory breakdowns and whether a given hardware setup is sufficient. Upcoming features include runtime estimation and automated hyperparameter suggestions.

Estimate GPU memory for LLM fine-tuning with Red Hat AI