How to resolve CUDA Out of Memory (OOM) when loading 7B+ parameter LLMs using PyTorch and Hugging Face?
06:05 26 Aug 2026

I am trying to run local inference on a 7B parameter open-source LLM using `transformers` and `torch` on a GPU with 12GB VRAM.

When loading the standard model weights in FP16/FP32 precision using `AutoModelForCausalLM.from_pretrained()`, the execution fails immediately with `torch.cuda.OutOfMemoryError: CUDA out of memory`.

What is the standard configuration required to load and run inference on these models efficiently without exceeding VRAM limits?

python deep-learning pytorch cuda huggingface-transformers