Estimating Memory Requirements for Running Local Large Language Models

On November 6, 2026, DEV Community published a walkthrough on estimating the memory required to run a large language model locally. The piece proposes a simple formula to answer the question before a model is downloaded: need = weights + KV cache + overhead. A model is considered runnable when need stays within the machine's headroom, specifically when need <= 0.8 × usable memory. The article notes that this estimation approach is used by the desktop app osFoundry, in its local model browser, and that the numbers were verified against the values the app displays on a 16GB M3 MacBook. The breakdown also covers the weights component, whose size is tied to the model's parameter count. According to the piece, this three-part estimate gives users a practical way to check fit before committing to a download, rather than relying on guesswork about available memory.