The guide discusses the challenges of fine-tuning Large Language Models (LLMs) due to GPU memory constraints, particularly VRAM bottlenecks. A general rule of thumb for full fine-tuning with 16-bit precision is 16GB of GPU memory per 1 billion parameters in the model. For a 7B parameter model, the estimated total VRAM requirements are approximately 70GB when using half-precision and 8-bit optimizers. Techniques like LoRA (Low-Rank Adaptation) and QLoRA (Quantized LoRA) significantly reduce VRAM requirements by up to 80% in some cases, making efficient fine-tuning possible for larger models. The guide provides a comparison table of VRAM requirements for different model sizes and fine-tuning techniques, highlighting the importance of considering VRAM constraints when training LLMs.