If you’ve spent any time in the open-source AI community recently, you’ve probably seen someone excitedly announce they are running a 70B parameter model locally, only to follow up an hour later asking why their system crashed with an OOM (Out of Memory) error. Deploying Large Language Models (LLMs) locally—whether for privacy, cost savings, or offline availability—is the new frontier for developers. But unlike deploying a standard web app where you just spin up an AWS EC2 instance and forget about it, deploying LLMs requires precise hardware mathematics. If you guess your VRAM (Video RAM) requirements, you will either overpay for GPUs you don't need, or your inference will crash entirely. Today, we're breaking down the exact math behind LLM VRAM consumption, the impact of quantization, and how to calculate your hardware needs before you hit deploy. The Core Equation: Parameters to Gigabytes The foundational rule of LLMs is simple: Parameters dictate memory.…