Local AI Models Filling Your Disk? Where Ollama, Hugging Face, and LM Studio Keep Models and How to Remove Them

A 7B or 8B parameter model still takes 4 to 5 GB after 4-bit quantization. Try a few sizes and quantization levels and tens of gigabytes disappear fast. To make it harder, every tool has its own model folder, and some of them are hidden. This guide lists the default locations of the common tools and how to delete models in each, then explains what WashBear can do for you.

#Where models live

Python libraries that download models through Hugging Face, such as transformers and diffusers, share the same cache. All of these locations can be changed: Ollama uses the OLLAMA_MODELS environment variable, Hugging Face uses HF_HOME or HF_HUB_CACHE, and LM Studio lets you change it on the app's My Models page.

#Think before you delete

• The cost of downloading again: models are often several gigabytes, and re-downloading on a slow connection takes a long time. Keep the ones you use regularly.
• Back up models you made yourself: models created from a Modelfile, models you imported or converted, and weights you fine-tuned may not be downloadable again.
• Files are shared: several Ollama models can share the same data, and recent versions of huggingface_hub also share identical files between repositories. Deleting one model may free less space than its listed size. Do not delete individual files inside blobs by hand; other models may still use them.

#Clean up manually

#Ollama

# List downloaded models and their sizes
ollama list
# Delete a model; include the tag after the colon
ollama rm llama3.2:3b
# Actual space used by the model folder
du -sh ~/.ollama/models

ollama rm deletes only the data unique to that model; anything other models still use is kept. ollama ps and ollama stop are about memory: the first shows loaded models, the second unloads one, and neither frees disk space. To use a different model folder, see the Ollama documentation.

#Hugging Face

# List cached repositories with size and last access time
hf cache ls
# Preview deleting an entire model repository; drop --dry-run to actually delete it
hf cache rm model/organization/model-name --dry-run
# Remove old revisions no branch or tag references, plus leftovers from interrupted downloads
hf cache prune

These are the commands in the current huggingface_hub documentation; the hf command line is installed with huggingface_hub. Subcommand names have changed between versions, so if a command is not found, run hf cache --help or upgrade huggingface_hub. ~/.cache/huggingface/xet holds the Xet storage cache used for uploads and transfers, and you can delete the whole folder; if an upload was interrupted, it has to start over afterwards.

#LM Studio

LM Studio's My Models page lists every downloaded model; delete one from its menu. This is also where you change the model folder. In Terminal, lms ls lists your local models. If you changed the folder, go by the path shown in My Models.

#Review and clean up with WashBear

The AI models and caches category in Deep Cleanup lists models these tools downloaded to their default locations:

• Ollama local model library: Ollama models share files, so WashBear treats the whole library as one item that can only be removed as a whole. If you still use any model in it, leave it unselected and delete models one by one with ollama rm. Quit Ollama before cleaning.
• Hugging Face local models: each models-- repository is listed separately, so you can select only the models you no longer use. Datasets are not included.
• LM Studio local models: listed per model folder. Only ~/.lmstudio/models is checked, and LM Studio must be closed before cleaning.
• WashBear also supports models from Whisper, PyTorch Hub, ModelScope, Keras, OpenAI CLIP, TensorFlow Hub, Coqui TTS, GPT4All, Jan, and ComfyUI, plus the Hugging Face download cache used for transfers.

Smart recommendation never preselects these model items; you confirm each one yourself. Two small caches appear under User cache files: LM Studio cache and Ollama downloaded updates (old update archives older than seven days). They contain no models and are preselected by Smart recommendation.

Cleanup deletes files directly instead of moving them to the Trash, and the result is recorded in Operation History. To see how much space your models take in total, download WashBear and run a scan; scanning alone never deletes anything. The demo below uses sample data and is only illustrative.

#Find stray model files

.gguf and .safetensors files downloaded straight from the web often end up in Downloads or Documents, outside every tool's model folder. Scan those locations with Large File Cleanup and filter by AI models to see large files in formats such as .gguf, .safetensors, .ckpt, .pt, and .onnx in one place. Ollama's model data has no file extension, so it does not appear in this filter; manage it as described above. To see which folders are largest, use Disk Space Analysis. The demo below also uses sample data.

#FAQ

#Can I use a deleted model again?

Yes, just download it again: ollama pull for Ollama, the in-app download for LM Studio, and libraries that load models through Hugging Face download them automatically on next use. Models you created or imported yourself are the exception, so back them up first.

#Why do the sizes in ollama list add up to more than the folder?

Models with different names or tags can share the same data, and ollama list counts each model separately, so the total is inflated. du -sh ~/.ollama/models shows the real usage.

#Can I move models to an external drive?

Yes. Set OLLAMA_MODELS for Ollama and HF_HOME or HF_HUB_CACHE for Hugging Face, and change the folder in My Models for LM Studio. After the move, WashBear finds the new Ollama and Hugging Face locations only if it can read those variables, and custom LM Studio folders are not checked. Use Disk Space Analysis to look at those locations.