LLM Memory Calculator: Online Estimators Miss 40% Usage

#llm #gpumemory #productionml #inferenceoptimizatio

The 24GB Myth

You plug your model specs into an online LLM memory calculator. Llama 2 70B, 4-bit quantization, 4096 context length. The calculator says 24GB. You provision a single A10G GPU on AWS, deploy your API, and watch it crash with OutOfMemoryError at the third concurrent request.

The calculator wasn't lying. It just wasn't counting.

Most online estimators calculate model weights only—the static memory footprint of parameters loaded into VRAM. They ignore KV cache growth, framework overhead, CUDA context allocation, and the memory spike from batch processing. In production, these "invisible" allocations routinely consume 30-50% of your total GPU budget. The gap between estimate and reality can mean the difference between 2 concurrent users and 8.

Here's what actually happens when you run a local LLM, and how to calculate memory requirements that survive first contact with production traffic.