How RapidFire AI Lets You Run Multiple TRL Trainings Concurrently on a Single GPU

Imagine you are trying to fine-tune a large language model, and you have several ideas for configurations you want to test. Normally, you would have to train each configuration one after the other, which can take hours or even days. With RapidFire AI, you can run all these configurations at the same time on a single GPU, because it efficiently shares the GPU memory and compute resources across all the experiments. This means you can compare results much faster and decide early which configurations are worth pursuing.

The system works by taking your dataset and slicing it into smaller chunks. Then, it uses a round-robin approach to cycle through different training configurations, giving each a slice of compute time. This is all managed in a way that maximizes GPU utilization without requiring more hardware. You can start, stop, and even clone running experiments from a dashboard, making it incredibly interactive. Plus, it integrates seamlessly with popular machine learning libraries like TRL, PEFT, and Transformers, so you do not need to rewrite your code.

• RapidFire AI uses a technique called “chunking” where it divides the dataset into smaller parts
• It then uses a scheduler to run multiple training runs concurrently, switching between them in a round-robin fashion
• This means while one model is waiting for data, another can use the GPU, leading to much higher efficiency
• You can stop, resume, or even clone a running experiment from a dashboard, making it very interactive
• It works with popular machine learning tools like SFT, DPO, and GRPO, and supports PEFT for lightweight fine-tuning

In essence, RapidFire AI turns the traditionally slow and sequential process of hyperparameter tuning into a parallel and interactive one. Instead of waiting days to compare ten different configurations, you can run them all at once and see results in hours. This is especially useful for researchers and engineers who need to iterate quickly, such as when comparing different learning rates, model sizes, or reward functions. The ability to stop, resume, and clone runs from a dashboard adds a level of interactivity previously unseen in these tools.