> For the complete documentation index, see [llms.txt](https://boinc-ai.gitbook.io/accelerate/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://boinc-ai.gitbook.io/accelerate/how-to-guides/knowing-how-big-of-a-model-you-can-fit-into-memory.md).

# Knowing how big of a model you can fit into memory

## Understanding how big of a model can fit on your machine

One very difficult aspect when exploring potential models to use on your machine is knowing just how big of a model will *fit* into memory with your current graphics card (such as loading the model onto CUDA).

To help alleviate this, 🌍 Accelerate has a CLI interface through `accelerate estimate-memory`. This tutorial will help walk you through using it, what to expect, and at the end link to the interactive demo hosted on the 🌍 Hub which will even let you post those results directly on the model repo!

Currently we support searching for models that can be used in `timm` and `transformers`.

This API will load the model into memory on the `meta` device, so we are not actually downloading and loading the full weights of the model into memory, nor do we need to. As a result it’s perfectly fine to measure 8 billion parameter models (or more), without having to worry about if your CPU can handle it!