Keep your PyTorch model in VRAM by hot swapping code
Pytorch script hot swap: Change code without unloading your LLM from VRAM - valine/training-hot-swap
Keep your PyTorch model in VRAM by hot swapping code launched on April 21, 2025 with 77 votes, #553 of 7,818 launches that month and more than 95% of that year's launches. The site was still online in September 2026, 1 year on.
In plain words
This tool helps developers manage PyTorch model memory by automatically swapping code in and out of VRAM during execution. It's designed for machine learning engineers working with large models who need to optimize GPU memory usage without manually managing model loading and unloading. The software enables keeping models active while reducing memory overhead through intelligent code hot-swapping techniques.
written from the facts on this page · September 2026
From the sources
This is an example of how to hotswap PyTorch training code without unloading your model weights from VRAM. For large LLMs it can take upwards of 30 seconds to load a model from disk to VRAM. Waiting 30 seconds every time you want to rerun your script slows down development. This is a barebones implementation of a method to keep large models in VRAM even after your training script exits. If a model reload is necessary, it happens in the background after exit ensuring the model will be ready immediately the next time your script is run. This works by spawning a second process that stays active after your target script exits. The script you change is not run directly. Instead, this background…from github.com
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