How Much VRAM You Actually Need to Run a 70B Parameter LLM Locally

Running a 70-billion-parameter large language model locally requires far more GPU memory than most consumer hardware offers, with raw FP16 weights alone demanding around 140 GB of VRAM. Quantization techniques reduce this significantly — a 4-bit quantized 70B model needs roughly 35 GB — making it feasible on high-end workstation GPUs or multi-GPU setups. However, advertised quantization levels can be misleading, as formats vary in overhead and metadata, meaning actual memory usage often exceeds theoretical minimums. CPU offloading allows models larger than available VRAM to run by storing some layers in system RAM, but this comes at a steep performance cost due to lower memory bandwidth. Experts recommend prioritising the highest-quality quantization that fits comfortably within available hardware, rather than simply chasing the smallest possible model size.