How to Calculate VRAM Needs for Running Large Language Models Locally

As open-weight models like Qwen 3.8, Llama 3.3, and DeepSeek-Coder increasingly rival proprietary AI APIs, more developers are shifting to local, self-hosted AI setups. Running these models requires careful VRAM planning, with total memory needs split across model weights, the KV cache, and a runtime buffer. Quantization significantly reduces weight memory — a 27B model at 4-bit precision needs roughly 15.2 GB, compared to around 54 GB at full FP16. The KV cache is a frequently overlooked memory cost, with long context windows of 128k tokens potentially adding 8–14 GB of extra VRAM on top of model weights. A free browser-based tool called Local LLM VRAM Sizer has been released to help users estimate hardware requirements based on model size, quantization level, and context window length.