Why Your 24 GB GPU Doesn't Actually Give Your LLM 24 GB of Memory

A common misconception among local LLM users is that a model file smaller than a GPU's listed VRAM will run without issues, but available memory is significantly less than the physical spec. Display drivers, runtime buffers, and model weights all compete for the same VRAM, leaving roughly 90 percent of listed capacity usable at best. Beyond weights, the KV cache — which stores context tokens for inference — can consume several additional gigabytes depending on context length and concurrent requests. For example, a 32B model at 4-bit quantization with a 32K context window and 20 percent runtime headroom requires about 27.5 GiB, exceeding what a single 24 GB card can reliably provide. Accurate deployment planning requires accounting for checkpoint size, usable VRAM, KV cache from actual model architecture, and runtime overhead before declaring a model fit for local use.