Programming Tutorials

  You have successfully downloaded the Llama 3 8B weights. You set up your PyTorch environment, configured a basic LoRA adapter, and launched the training script on your RTX 3090 or Google Colab T4. Then, before the first epoch even starts, you hit the wall:  RuntimeError: CUDA out of memory. Tried to allocate... This is the single most common barrier for engineers moving from using LLMs to fine-tuning them. It is frustrating because the math  seems  like it should work. If Llama 3 8B is roughly 15GB in half-precision (FP16), why does it crash a 24GB or even 40GB card? The answer lies in the hidden memory overhead of the training process itself. This guide provides a root cause analysis of VRAM consumption and a production-grade code solution using QLoRA (Quantized LoRA) to fit Llama 3 training pipelines onto consumer hardware. The Root Cause: Where Did the VRAM Go? To fix the OOM (Out Of Memory) error, you must understand what consumes GPU memory. It is not just the...