# Determining if a model will run locally

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**Category:** Beginners\
**Created:** [April 6, 2025, 5:25am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fdetermining-if-a-model-will-run-locally%2F149068 "2025-04-06T05:25:26Z")\
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**Author:** ![John6666](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/john6666/32/27664_2.png) [@John6666](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2FJohn6666)%5C
**Post date:** [April 6, 2025, 10:10am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fdetermining-if-a-model-will-run-locally%2F149068%2F2 "2025-04-06T10:10:37Z")

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Basically, it is determined in proportion to the file size of the model weight. There are several changes depending on whether or not to quantize with what bit, and if not quantized, whether to load with 32 bits or 16 bits.  
In addition to the model size, VRAM is consumed according to the length of the generated context.

Roughly speaking, assuming 4-bit quantization, if the VRAM capacity is about the same as the model size in terms of the number of billion (B) parameters, it will work. (If you want to run an 4-bit quantized 8B model, you’ll need about 8GB. If your model is about 4.5GB, and you want to use the rest for inference… if it’s a 12B model, you’ll need about 12GB. This is just a rough guide.)

And since RoBerta is about **0.1B** , you should be fine without quantization with 24GB VRAM.

> **[LLM Model VRAM Calculator - a Hugging Face Space by NyxKrage](https://huggingface.co/spaces/NyxKrage/LLM-Model-VRAM-Calculator)**
>
> Discover amazing ML apps made by the community

> [@How to quickly determine memory requirements for model](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fhow-to-quickly-determine-memory-requirements-for-model%2F43426%2F4)%3A
>
> How would I determine these requirements when I’m finetuning a model? I’m finetuning a 780 MB model with a 5 MB dataset. On my local machine this runs fine (16 GB ram) but when I use a GPU (Tesla T4 with 16 GB of GPU ram), I immediately get an OOM error when I launch trainer.train().

> **[Model memory estimator](https://huggingface.co/docs/accelerate/main/en/usage_guides/model_size_estimator)**
>
> We’re on a journey to advance and democratize artificial intelligence through open source and open science.

 ![robertamodelsize](https://us1.discourse-cdn.com/hellohellohello/original/3X/5/d/5ddb5e300486cb703ecf850eeb2419d98ea23d00.png)

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