# Determining if a model will run locally **URL:** \ **Category:** Beginners\ **Created:** [April 6, 2025, 5:25am UTC](https://discuss.huggingface.co/t/determining-if-a-model-will-run-locally/149068 "2025-04-06T05:25:26Z")\ **Posts on this page:** 5\ **Page:** 1
**Author:** ![hrishim](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/hrishim/32/44891_2.png) [@hrishim](https://discuss.huggingface.co/u/hrishim)\ **Post date:** [April 6, 2025, 5:25am UTC](https://discuss.huggingface.co/t/determining-if-a-model-will-run-locally/149068/1 "2025-04-06T05:25:26Z")
I have a GPU with 24GB VRAM. How can I determine if a model I see on HF can run on my machine locally? For example, from this model card [deepset/roberta-base-squad2 · Hugging Face](https://huggingface.co/deepset/roberta-base-squad2) the model is a 124M parameter model. I think that should run but I am not sure. ---
**Author:** ![John6666](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/john6666/32/27664_2.png) [@John6666](https://discuss.huggingface.co/u/John6666)\ **Post date:** [April 6, 2025, 10:10am UTC](https://discuss.huggingface.co/t/determining-if-a-model-will-run-locally/149068/2 "2025-04-06T10:10:37Z")
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](https://discuss.huggingface.co/t/how-to-quickly-determine-memory-requirements-for-model/43426/4): > > 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) ---
**Author:** ![hrishim](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/hrishim/32/44891_2.png) [@hrishim](https://discuss.huggingface.co/u/hrishim)\ **Post date:** [April 6, 2025, 2:11pm UTC](https://discuss.huggingface.co/t/determining-if-a-model-will-run-locally/149068/3 "2025-04-06T14:11:09Z")
Thank you. How can I tell what the quantization of a given model? The one I linked to says it is F32-I64. So it will not have 4-bit quantization correct? ---
**Author:** ![John6666](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/john6666/32/27664_2.png) [@John6666](https://discuss.huggingface.co/u/John6666)\ **Post date:** [April 6, 2025, 3:51pm UTC](https://discuss.huggingface.co/t/determining-if-a-model-will-run-locally/149068/4 "2025-04-06T15:51:01Z")
Yes. In the case of RoBerta, it is probably uploaded as 32-bit float? Well, type conversion is done by yourself as needed when loading (torch\_dtype=torch.\*\*\*). If you don’t do it, it will be loaded in the format it was uploaded in, or if it can’t be done, it will be loaded after converting it to a format that will not cause any problems. Some are already quantized, but if they are not, you can do it yourself when loading (on-the-fly quantization). It depends on the quantization algorithm, but in general, the smaller the size, the lower the precision. On the other hand, for calculations, the faster the format is for the GPU, the faster it will be on the GPU, so for example, bfloat16 is faster for inference on GeForce 30x0 and later, and the precision is also relatively good (if you ignore the bug related to attention). Also, there are differences in speed and precision between quantization and inference for each quantization algorithm. For example, EXL2 takes a long time to quantize, but it is fast. GGUF is used a lot because it is compatible with many software programs and the converted files are standalone, and bitsandbytes’ NF4 is used a lot because it was relatively quick to support it with the Hugging Face library and it is easy to use for on-the-fly quantization. > **[Making LLMs even more accessible with bitsandbytes, 4-bit quantization and QLoRA](https://huggingface.co/blog/4bit-transformers-bitsandbytes)** > > We’re on a journey to advance and democratize artificial intelligence through open source and open science. > **[Models](https://huggingface.co/docs/transformers/main_classes/model#transformers.PreTrainedModel.from_pretrained.torch_dtype)** > > We’re on a journey to advance and democratize artificial intelligence through open source and open science. > **[Common AI Model Formats](https://huggingface.co/blog/ngxson/common-ai-model-formats)** > > A Blog post by Xuan-Son Nguyen on Hugging Face ---
**Author:** ![aaac12345](https://avatars.discourse-cdn.com/v4/letter/a/82dd89/32.png) [@aaac12345](https://discuss.huggingface.co/u/aaac12345)\ **Post date:** [April 7, 2025, 9:01am UTC](https://discuss.huggingface.co/t/determining-if-a-model-will-run-locally/149068/5 "2025-04-07T09:01:28Z")
Of course, you can configure your zram memory and tune up all your resources to make it run smoothly even with a training package. A llama model is very light .