> ## Content Index
> Fetch the complete content index at: https://en.blog.themarfa.name/llms.txt
> Use this file to discover other available public pages before exploring further.

# How to Quickly Calculate the GPU Needed to Run LLMs Locally
- URL: https://en.blog.themarfa.name/how-to-quickly-calculate-the-gpu-needed-to-run-llms-locally/
- Published: 2025-08-20T12:00:13.000Z
- Updated: 2025-08-20T12:00:13.000Z
- Description: SelfHostLLM lets you check whether your GPU has enough power to run an LLM such as Llama or Mistral, and it calculates the optimal configuration for the model you choose.
- Author: Konstantin Dokuchaev
- Tags: web, SelfHostLLM

Running a neural model on your own computer has become far easier than when they first appeared. That said, it still often requires a powerful GPU setup to run them correctly. How can you be sure your PC can handle the next ChatGPT‑style model?

You can use the [**SelfHostLLM**](https://selfhostllm.org/) website, which helps you determine the required GPU memory and the number of simultaneous LLM requests you can handle when deploying the models locally. The site supports Llama, Qwen, DeepSeek, Mistral, and many others.

![](https://storage.ghost.io/c/31/c7/31c7da9c-f519-464c-9a3b-2d4144b557a6/content/images/2025/08/image-13.png)

### How it works

1. On the left‑hand side of the page, enter the specifications of your current GPU(s).
2. On the right, choose the desired LLM model and its parameters.
3. Click **Calculate configuration**.
4. You’ll receive a report telling you whether your hardware will be sufficient, and if so, how many concurrent requests you can run safely.

![](https://storage.ghost.io/c/31/c7/31c7da9c-f519-464c-9a3b-2d4144b557a6/content/images/2025/08/image-14.png)

This quick test can save you time and money by letting you know in advance if you need to upgrade your GPU or adjust your deployment strategy.

[Try SelfHostLLM](https://selfhostllm.org/)