15-Line JavaScript Formula Estimates VRAM Needed to Run LLMs Locally
A technical guide published by EU hardware retailer Mineshop.eu explains how to estimate GPU VRAM requirements for running large language models locally using a compact JavaScript function. The calculation accounts for three key components: model weights (parameter count multiplied by bits-per-parameter), the KV cache (which stores key-value vectors per layer and token), and a runtime headroom reserve of around 2 GiB. For example, running a 4-bit quantised 8B-parameter model at an 8,192-token context requires approximately 7.28 GiB of VRAM in total. Larger models and longer contexts scale the requirement significantly — a 70B model at 32K context needs nearly 50 GiB. The post includes worked examples and test assertions to validate the formula's accuracy across different model sizes and cache configurations.
