How Much VRAM Do You Really Need to Run a 70B LLM?

Running a 70B LLM locally requires more than just considering parameter counts. To determine if a model fits within 24 GB to 80 GB of VRAM, users must evaluate model weights, quantization, KV cache, and runtime overhead. If the model exceeds available VRAM, strategies like CPU offloading and multi-GPU inference become necessary. Understanding these four key components is essential for estimating the actual memory needed to operate large language models on local hardware.