Programming Tutorials

  You have likely attempted to load Meta’s Llama 3 70B Instruct model using Hugging Face’s   AutoModelForCausalLM   on a machine equipped with an RTX 3090 or 4090. Shortly after execution, you were likely greeted by a fatal   torch.cuda.OutOfMemoryError . This is the barrier to entry for high-parameter LLMs. While 8B models run effortlessly on modern consumer hardware, the 70B parameter variant is a massive logistical challenge. This guide details exactly how to bypass these memory constraints using GGUF quantization and intelligent layer offloading via  llama.cpp  and Python. We will move from a crashing script to a functional inference engine running on a single 24GB VRAM card backed by system RAM. The Root Cause: The Arithmetic of VRAM To solve the memory bottleneck, we must first audit the memory requirements. The standard distribution of Llama 3 is in FP16 (16-bit floating point) precision. The math for VRAM usage is straightforward but unforgiving: To...