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Originally created by @DanielMazurkiewicz on GitHub (Dec 19, 2023).
Original GitHub issue: https://github.com/ollama/ollama/issues/1600
As in the title. I want to unload model, is there any option for it?
@zach030 commented on GitHub (Dec 19, 2023):
No support yet, you can only shutdown the serve to close the model
@technovangelist commented on GitHub (Dec 19, 2023):
The model gets automatically unloaded after 5 minutes. It sounds like you want it unloaded in less time than that? Or are you saying it's taking longer than 5 minutes?
@igorschlum commented on GitHub (Dec 19, 2023):
I think that it could be interesting to set a parameter for the 5 minutes. Sometimes, you want less or more, depending the usage of Ollama you make.
@mattbisme commented on GitHub (Dec 20, 2023):
There are some use cases where it would be nice to have it unload almost immediately (single-turn Siri request). But other times where it would be better to have it loaded indefinitely (a chatbot that is prompted occasionally where a fast response provides a better experience).
Being able to configure this parameter at model runtime (especially via API) would be great!
@chymian commented on GitHub (Dec 27, 2023):
+1 for the API- & Modelfile-tuneable unload parameter, especially: keep-inifinit
@dennisorlando commented on GitHub (Jan 6, 2024):
Something as simple as "ollama load" and "ollama unload" would suffice
@olekse commented on GitHub (Jan 6, 2024):
^^^
edit. btw for me it always stays in ram (Windows 10/WSL /w https://github.com/ollama-webui/ollama-webui)
@pdevine commented on GitHub (Jan 28, 2024):
#2146 adds this which will be available in
0.1.23. Going to go ahead and close this. You can setkeep_aliveto-1when calling the chat API and it will leave the model loaded in memory.@nathanleclaire commented on GitHub (Feb 7, 2024):
@pdevine For what it's worth I would still like the ability to manually evict a model from VRAM through API + CLI command. The keepalive functionality is nice but on my Linux box (will have to double-check later to make sure it's latest version, but installed very recently) after a chat session the model just sits there in VRAM and I have to restart ollama to get it out if something else wants to use the GPU. Which, of course, is a shame because I would also like to
ollama pullthings at the same time :)@pdevine commented on GitHub (Feb 7, 2024):
@nathanleclaire I've been thinking about adding an
OLLAMA_KEEP_ALIVEenv variable to be able to change the default timeout. I don't want to go too extreme here though because ideally there would be much richer controls (e.g. access controls/policies/multiple models/etc.)@OliChase404 commented on GitHub (Feb 21, 2024):
This might help, from the faq.md file:
How do I keep a model loaded in memory or make it unload immediately?
By default models are kept in memory for 5 minutes before being unloaded. This allows for quicker response times if you are making numerous requests to the LLM. You may, however, want to free up the memory before the 5 minutes have elapsed or keep the model loaded indefinitely. Use the
keep_aliveparameter with either the/api/generateand/api/chatAPI endpoints to control how long the model is left in memory.The
keep_aliveparameter can be set to:For example, to preload a model and leave it in memory use:
To unload the model and free up memory use:
@OliChase404 commented on GitHub (Feb 21, 2024):
So to immediately unload a model and free your vram run:
curl http://localhost:11434/api/generate -d '{"model": "MODELNAME", "keep_alive": 0}'
Replace MODELNAME with the name of the model currently loaded.
@olekse commented on GitHub (Mar 18, 2024):
What about removing the model from the RAM? It still stays in RAM when VRAM is cleared.
@pdevine commented on GitHub (Mar 18, 2024):
@olekse it's the same.
@wenhui01 commented on GitHub (Apr 15, 2024):
This parameter can't work with embedding model.
@Divelix commented on GitHub (May 5, 2024):
CLI command to immediately unload model from memory is a must have for me. Most of the time 5 mins is fine, but occasionally I have to shut it down right now to use full VRAM for smth else. Implement what @dennisorlando suggested above, please.
@VfBfoerst commented on GitHub (May 8, 2024):
worked for me :)
@davidearlyoung commented on GitHub (Jun 2, 2024):
Since terminal command line
ollama psis a thing now, as well as loading multiple models, I feel that this should be revisited. Such as reconsidering adding the ability to target an already loaded model for unloading using terminal command line. Rather then sending a client request withkeep_aliveparameter to 0 when ever I want to evict a select model from vram/ram. I feel that the keep_alive method to target unloading a single model is a bit cumbersome. Especially from a server admin's perspective.@eav-solution commented on GitHub (Jun 19, 2024):
Vote
@Dement242 commented on GitHub (Aug 16, 2024):
Here is a script that will list all running models on the ip´s (edit the script and change to your ip-addresses) and unload the model you select.
@MoreColors123 commented on GitHub (Aug 25, 2024):
i was battling with full VRAM while using ollama and FLUX simultaneously. here is a version for anyone looking for a solution for WINDOWS. it is written with chat gpt, so i don't know how, but it works. just adjust the IP which ollama is running on in the first line. Below i also included a .bat script to execute this .ps1
/// put this in a file called unload.ps1
/// put this in a .bat file:
-> double the click .bat file to choose the ollama model to unload
@pdevine commented on GitHub (Nov 17, 2024):
To unload a model, just use
ollama stop <model>. To keep it in memory you can useollama run --keepalive -1s <model>@somera commented on GitHub (Feb 27, 2025):
Instead of
it works for me with:
@mbylstra commented on GitHub (Mar 12, 2025):
I don't think that's possible from the REST API (or the Python client). It'd be great if there was a straightforward endpoint to immediately unload a model. As I understand it is possible with
/api/generate -d '{"model": "MODELNAME", "keep_alive": 0}', but as mentioned previously it is a cumbersome and counterintuitive way to do it. I don't think it'd hurt to add a specific endpoint for this.A common use case is sharing a GPU between image generation and text generation (and needing to unload from memory when switching from text generation to image generation if the GPU does not have enough VRAM for both)
@dstadulis commented on GitHub (Sep 12, 2025):
Offered is a solution which:
keepalivevalue to 0, effectively purging the model from memory