> For the complete documentation index, see [llms.txt](https://docs.kodesage.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.kodesage.ai/deploy-kodesage/self-hosted-llm-installation.md).

# Self-hosted LLM installation

**Prerequisites** 

* A system with Ubuntu with docker and docker-compose installed (as mentioned before) and with the latest Nvidia driver for the GPU-s in the system. 
* Install the [Nvidia Container Toolkit.](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) 
* Install git-lfs with the command: sudo apt-get install git-lfs 

Copy the provided text-generation-webui folder to a server with one or more Nvidia GPU-s.  

**With multiple GPU-s** 

You can modify the compose file to add more instances if you have more GPU-s. For this copy the part from the  ‘text-generation-webui-docker-1:’ as many times as the number of GPU-s in the system. Change the ‘device\_ids:’ part to the corresponding GPU id, which you can check by executing the command ‘nvidia-smi’ in the system’s terminal. 

Change the service and the container name to a different one than the first one (change the number). 

Finally, change the ports exposed to a different one than the first for each instance. 

**Download the model**

We will provide you with the most up-to-date LLM model during onboarding. But you can also use the model of your choice.


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