Local LLaMA Server Setup Documentation

TL;DR: A guide to setting up a fully local and private language model server using ollama.

About

This repository outlines the steps to run a server for running local language models. It uses Debian specifically, but most Linux distros should follow a very similar process. It aims to be a guide for Linux beginners like me who are setting up a server for the first time.

The process involves installing the NVIDIA drivers, setting the GPU power limit, and configuring the server to run ollama at boot. It also includes setting up auto-login and scheduling the init.bash script to run at boot. All these settings are based on my ideal setup for a language model server that runs most of the day but a lot can be customized to suit your needs. For example, you can use any OpenAI-compatible server like llama.cpp or LM Studio instead of ollama.

System Requirements

Any modern CPU and GPU combination should work for this guide. Previously, compatibility with AMD GPUs was an issue but the latest releases of ollama have worked through this and AMD GPUs are now supported natively.

For reference, this guide was built around the following system:

CPU: Intel Core i5-12600KF
Memory: 32GB 6000 MHz DDR5 RAM
Storage: 1TB M.2 NVMe SSD
GPU: Nvidia RTX 3090 24GB

Note for AMD users: Power limiting is skipped for AMD GPUs as AMD has recently made it difficult to set power limits on their GPUs. Naturally, skip any steps involving nvidia-smi or nvidia-persistenced and the power limit in the init.bash script.

Note for CPU-only users: You can skip the driver installation and power limiting steps. The rest of the guide should work as expected.

Prerequisites

Fresh install of Debian
Internet connection
Basic understanding of the Linux terminal
Peripherals like a monitor, keyboard, and mouse

To install Debian on your newly built server hardware,

Download the Debian ISO from the official website.
Create a bootable USB using a tool like Rufus for Windows or Balena Etcher for MacOS.
Boot into the USB and install Debian.

For a more detailed guide on installing Debian, refer to the official documentation. For those who aren't yet experienced with Linux, I recommend using the graphical installer - you will be given an option between the text-based installer and graphical installer.

I also recommend installing a lightweight desktop environment like XFCE for ease of use. Other options like GNOME or KDE are also available - GNOME may be a better option for those using their server as a primary workstation as it is more feature-rich (and, as such, heavier) than XFCE.

Essential Setup

Update the system
- Run the following command:
```
sudo apt update
```

Install drivers

Nvidia
- Run the following commands:
```
apt install linux-headers-amd64
apt install nvidia-driver firmware-misc-nonfree
```
- Reboot the server.
- Run the following command to verify the installation:
```
nvidia-smi
```

AMD

Run the following commands:

deb http://deb.debian.org/debian bookworm main contrib non-free-firmware
apt-get install firmware-amd-graphics libgl1-mesa-dri libglx-mesa0 mesa-vulkan-drivers xserver-xorg-video-all

Reboot the server.

Install ollama
- Download ollama from the official repository:
```
curl -fsSL https://ollama.com/install.sh | sh
```
- (Recommended) We want our API endpoint to be reachable by the rest of the LAN. For ollama, this means setting OLLAMA_HOST=0.0.0.0 in the ollama.service.
  - Run the following command to edit the service:
```
systemctl edit ollama.service
```
  - Find the [Service] section and add Environment="OLLAMA_HOST=0.0.0.0" under it. It should look like this:
```
[Service]
Environment="OLLAMA_HOST=0.0.0.0"
```
  - Save and exit.
  - Reload the environment.
```
systemctl daemon-reload
systemctl restart ollama
```
Create the init.bash script

This script will be run at boot to set the GPU power limit and start the server using ollama. We set the GPU power limit lower because it has been seen in testing and inference that there is only a 5-15% performance decrease for a 30% reduction in power consumption. This is especially important for servers that are running 24/7.
- Run the following commands:
```
touch init.bash
nano init.bash
```
- Add the following lines to the script:
```
#!/bin/bash
sudo nvidia-smi -pm 1
sudo nvidia-smi -pl (power_limit)
ollama run (model)
ollama serve
```
  Replace (power_limit) with the desired power limit in watts. For example, sudo nvidia-smi -pl 250.
  
  Replace (model) with the name of the model you want to run. For example, ollama run mistral:latest.
  
  For multiple GPUs, modify the script to set the power limit for each GPU:
```
sudo nvidia-smi -i 0 -pl (power_limit)
sudo nvidia-smi -i 1 -pl (power_limit)
```
- Save and exit the script.
- Make the script executable:
```
chmod +x init.bash
```
Add init.bash to the crontab

Adding the init.bash script to the crontab will schedule it to run at boot.
- Run the following command:
```
crontab -e
```
- Add the following line to the file:
```
@reboot /path/to/init.bash
```
  Replace /path/to/init.bash with the path to the init.bash script.
- (Optional) Add the following line to shutdown the server at 12am:
```
0 0 * * * /sbin/shutdown -h now
```
- Save and exit the file.
Give nvidia-persistenced and nvidia-smi passwordless sudo permissions

We want init.bash to run the nvidia-smi commands without having to enter a password. This is done by editing the sudoers file.

AMD users can skip this step as power limiting is not supported on AMD GPUs.
- Run the following command:
```
sudo visudo
```
- Add the following lines to the file:
```
(username) ALL=(ALL) NOPASSWD: /usr/bin/nvidia-persistenced
(username) ALL=(ALL) NOPASSWD: /usr/bin/nvidia-smi
```
  Replace (username) with your username.
  
  IMPORTANT: Ensure that you add these lines AFTER %sudo ALL=(ALL:ALL) ALL. The order of the lines in the file matters - the last matching line will be used so if you add these lines before %sudo ALL=(ALL:ALL) ALL, they will be ignored.
- Save and exit the file.
Configure auto-login

When the server boots up, we want it to automatically log in to a user account and run the init.bash script. This is done by configuring the lightdm display manager.
- Run the following command:
```
sudo nano /etc/lightdm/lightdm.conf
```
- Find the following commented line. It should be in the [Seat:*] section.
```
# autologin-user=
```
- Uncomment the line and add your username:
```
autologin-user=(username)
```
  Replace (username) with your username.
- Save and exit the file.

Additional Setup

SSH

Enabling SSH allows you to connect to the server remotely. After configuring SSH, you can connect to the server from another device on the same network using an SSH client like PuTTY or the terminal. This lets you run your server headlessly without needing a monitor, keyboard, or mouse after the initial setup.

On the server:

Run the following command:
```
sudo apt install openssh-server
```
Start the SSH service:
```
sudo systemctl start ssh
```
Enable the SSH service to start at boot:
```
sudo systemctl enable ssh
```
Find the server's IP address:
```
ip a
```

On the client:

Connect to the server using SSH:
```
ssh (username)@(ip_address)
```
Replace (username) with your username and (ip_address) with the server's IP address.

If you expect to tunnel into your server often, I highly recommend following this guide to enable passwordless SSH using ssh-keygen and ssh-copy-id. It worked perfectly on my Debian system despite having been written for Raspberry Pi OS.

Firewall

Setting up a firewall is essential for securing your server. The Uncomplicated Firewall (UFW) is a simple and easy-to-use firewall for Linux. You can use UFW to allow or deny incoming and outgoing traffic to and from your server.

Install UFW:
```
sudo apt install ufw
```
Allow SSH, HTTPS, and any other ports you need:
```
sudo ufw allow ssh https 3000 11434 80 8000 8080
```
Here, we're allowing SSH (port 22), HTTPS (port 443), Open WebUI (port 3000), Ollama API (port 11434), HTTP (port 80), OpenedAI Speech (8000), and Docker (port 8080). You can add or remove ports as needed.
Enable UFW:
```
sudo ufw enable
```
Check the status of UFW:
```
sudo ufw status
```

Refer to this guide for more information on setting up UFW.

Open WebUI

In this step, we'll install Docker and Open WebUI. Docker is a containerization platform that allows you to run applications in isolated environments. Open WebUI is a web-based interface for managing Ollama models and chats, and provides a beautiful, performant UI for communicating with your models.

You will want to do this if you want to access your models from a web interface. If you're fine with using the command line or want to consume models through a plugin/extension, you can skip this step.

Docker

This subsection follows this guide to install Docker Engine on Debian.

Run the following commands:

# Add Docker's official GPG key:
sudo apt-get update
sudo apt-get install ca-certificates curl
sudo install -m 0755 -d /etc/apt/keyrings
sudo curl -fsSL https://download.docker.com/linux/debian/gpg -o /etc/apt/keyrings/docker.asc
sudo chmod a+r /etc/apt/keyrings/docker.asc

# Add the repository to Apt sources:
echo \
"deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.asc] https://download.docker.com/linux/debian \
$(. /etc/os-release && echo "$VERSION_CODENAME") stable" | \
sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
sudo apt-get update

Install the Docker packages:

sudo apt-get install docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin

Verify the installation:
```
sudo docker run hello-world
```

Open WebUI

Now that Docker is installed, we can install Open WebUI. To install without Nvidia GPU support, run the following command:

sudo docker run -d -p 3000:8080 --add-host=host.docker.internal:host-gateway -v open-webui:/app/backend/data --name open-webui --restart always ghcr.io/open-webui/open-webui:main

For Nvidia GPUs, run the following command:

sudo docker run -d -p 3000:8080 --gpus all --add-host=host.docker.internal:host-gateway -v open-webui:/app/backend/data --name open-webui --restart always ghcr.io/open-webui/open-webui:cuda

You can access it by navigating to http://localhost:3000 in your browser or http://(server_IP):3000 from another device on the same network. There's no need to add this to the init.bash script as Open WebUI will start automatically at boot via Docker Engine.

To update Open WebUI once, run the following command:

docker run --rm --volume /var/run/docker.sock:/var/run/docker.sock containrrr/watchtower --run-once open-webui

To keep it updated automatically, run the following command:

docker run -d --name watchtower --volume /var/run/docker.sock:/var/run/docker.sock containrrr/watchtower open-webui

OpenedAI Speech

OpenedAI Speech is a text-to-speech server that wraps Piper TTS and Coqui XTTS v2 in an OpenAI-compatible API. This is great because it plugs in easily to the Open WebUI interface, giving your models the ability to speak their responses.

WARNING: The documentation for this project leaves a little to be desired. I've run into a plethora of issues trying to get it to work and almost all of it has been trial and error by going through the code. I've done my best to provide a comprehensive guide here but be prepared to troubleshoot in case things have changed when you download it.

Piper TTS is a more lightweight, less performant model that is great for quick responses - it can also run CPU-only inference, which may be a better fit for systems that need to reserve as much VRAM for language models as possible. XTTS is a more performant model that requires a GPU for inference. Piper is:

generally easier to setup with out-of-the-box CUDA acceleration, and,
has a plethora of voices that can be found here, so it's what I would suggest you start with.

To install OpenedAI Speech, first clone the repository and navigate to the directory:
```
git clone https://github.com/matatonic/openedai-speech
cd openedai-speech
```
Create a new virtual environment named speech and activate it. Then, install piper-tts:
```
python3 -m venv speech
source speech/bin/activate
pip install piper-tts
```
This is a minimal virtual environment that is only required to run the script that downloads voices.
Download the relevant voices. If you intend to use both Piper and XTTS, download both. Otherwise, download the specific voices you need:
```
# for tts-1 / piper
bash download_voices_tts-1.sh
# for tts-1-hd / xtts
bash download_voices_tts-1-hd.sh
```
Copy the sample.env file to speech.env:
```
cp sample.env speech.env
```
To use the container as a service, also uncomment the following line:
```
#restart: unless-stopped
```
Edit the speech.env file:
- Using Piper
  
  To use Piper as the default model, no changes are required.
  
  To only use Piper and never load XTTS, uncomment the following CLI_COMMAND line:
```
#CLI_COMMAND="python speech.py --xtts_device none"
```
  To use CUDA acceleration with Piper, add the following CLI_COMMAND line:
```
CLI_COMMAND="python speech.py --piper_cuda"
```
- Using XTTS
  
  Uncomment the PRELOAD_MODEL and CLI_COMMAND lines:
```
#PRELOAD_MODEL=xtts
#CLI_COMMAND="python speech.py --preload $PRELOAD_MODEL"
```
Run the following command to start the server:
```
docker compose up -d
```

OpenedAI Speech runs on 0.0.0.0:8000 by default. You can access it by navigating to http://localhost:8000 in your browser or http://(server_IP):8000 from another device on the same network without any additional changes.

Open WebUI Integration

To integrate your OpenedAI Speech server with Open WebUI, navigate to the Audio tab under Settings in Open WebUI and set the following values:

Text-to-Speech Engine: OpenAI
API Base URL: http://host.docker.internal:8000/v1
API Key: anything-you-like
Set Model: tts-1 (for Piper) or tts-1-hd (for XTTS)

host.docker.internal is a magic hostname that resolves to the internal IP address assigned to the host by Docker. This allows containers to communicate with services running on the host, such as databases or web servers, without needing to know the host's IP address. It simplifies communication between containers and host-based services, making it easier to develop and deploy applications.

The TTS engine is set to OpenAI because OpenedAI Speech is OpenAI-compatible. There is no data transfer between OpenAI and OpenedAI Speech - the API is simply a wrapper around Piper and XTTS.

Downloading Voices

We'll use Piper here because I haven't found any good resources for high quality .wav files for XTTS. The process is the same for both models, just replace tts-1 with tts-1-hd in the following commands. We'll download the en_GB-alba-medium voice as an example.

bash download_voices_tts-1.sh en_GB-alba-medium

Update the voice_to_speaker.yaml file to include the voice you downloaded. This file maps the voice to a speaker name that can be used in the Open WebUI interface. For example, to map the en_GB-alba-medium voice to the speaker name alba, add the following lines to the file:

alba:
    model: voices/en_GB-alba-medium.onnx
    speaker: # default speaker

Run the following command:

sudo docker ps -a

Identify the container IDs of

OpenedAI Speech
Open WebUI

Restart both containers:

sudo docker restart (openedai_speech_container_ID)
sudo docker restart (open_webui_container_ID)

Replace (openedai_speech_container_ID) and (open_webui_container_ID) with the container IDs you identified.

Accessing Ollama

This section deals with accessing ollama from an extension/application/plugin where you have to specify the base URL to access your ollama models. Accessing ollama on the server itself is trivial. To test your endpoint, simply run:

curl http://localhost:11434/api/generate -d '{
  "model": "llama2",
  "prompt":"Why is the sky blue?"
}'

Replace llama2 with your preferred model.

Assuming the OLLAMA_HOST environment variable has been set to 0.0.0.0, accessing ollama from anywhere on the network is still trivial! Simply replace localhost with your server's IP.

Refer to Ollama's REST API docs for more information on the entire API.

Troubleshooting

For any service running in a container, you can check the logs by running sudo docker logs -f (container_ID). If you're having trouble with a service, this is a good place to start.

Nvidia drivers

Disable Secure Boot in the BIOS if you're having trouble with the Nvidia drivers not working. For me, all packages were at the latest versions and nvidia-detect was able to find my GPU correctly, but nvidia-smi kept returning the NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver error. Disabling Secure Boot fixed this for me. Better practice than disabling Secure Boot is to sign the Nvidia drivers yourself but I didn't want to go through that process for a non-critical server that can afford to have Secure Boot disabled.

`ollama`

If you receive the Failed to open "/etc/systemd/system/ollama.service.d/.#override.confb927ee3c846beff8": Permission denied error from Ollama after running systemctl edit ollama.service, simply creating the file works to eliminate it. Use the following steps to edit the file.
- Run:
```
sudo mkdir -p /etc/systemd/system/ollama.service.d
sudo nano /etc/systemd/system/ollama.service.d/override.conf
```
- Retry the remaining steps.
If you still can't connect to your API endpoint, check your firewall settings. This guide to UFW (Uncomplicated Firewall) on Debian is a good resource.

`ssh`

If you encounter an issue using ssh-copy-id to set up passwordless SSH, try running ssh-keygen -t rsa on the client before running ssh-copy-id. This generates the RSA key pair that ssh-copy-id needs to copy to the server.

Open WebUI

If you encounter Ollama: llama runner process has terminated: signal: killed, check your Advanced Parameters, under Settings > General > Advanced Parameters. For me, bumping the context length past what certain models could handle was breaking the ollama server. Leave it to the default (or higher, but make sure it's still under the limit for the model you're using) to fix this issue.

OpenedAI Speech

If you encounter docker: Error response from daemon: Unknown runtime specified nvidia. when running docker compose up -d, ensure that you have nvidia-container-toolkit installed (this was previously nvidia-docker2, which is now deprecated). If not, installation instructions can be found here. Make sure to reboot the server after installing the toolkit. If you still encounter issues, ensure that your system has a valid CUDA installation by running nvcc --version.
If nvcc --version doesn't return a valid response despite following Nvidia's installation guide, the issue is likely that CUDA is not in your PATH variable. Run the following command to edit your .bashrc file:
```
sudo nano /home/(username)/.bashrc
```
Replace (username) with your username. Add the following to your .bashrc file:
```
export PATH="/usr/local/(cuda_version)/bin:$PATH"
export LD_LIBRARY_PATH="/usr/local/(cuda_version)/lib64:$LD_LIBRARY_PATH"
```
Replace (cuda_version) with your installation's version. If you're unsure of which version, run ls /usr/local to find the CUDA directory. It is the directory with the cuda prefix, followed by the version number.

Save and exit the file, then run source /home/(username)/.bashrc to apply the changes (or close the current terminal and open a new one). Run nvcc --version again to verify that CUDA is now in your PATH. You should see something like the following:
```
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2024 NVIDIA Corporation
Built on Thu_Mar_28_02:18:24_PDT_2024
Cuda compilation tools, release 12.4, V12.4.131
Build cuda_12.4.r12.4/compiler.34097967_0
```
If you see this, CUDA is now in your PATH and you can run docker compose up -d again.
If you run into a VoiceNotFoundError, you may either need to download the voices again or the voices may not be compatible with the model you're using. Make sure to check your speech.env file to ensure that the PRELOAD_MODEL and CLI_COMMAND lines are configured correctly.

Monitoring

To monitor GPU usage, power draw, and temperature, you can use the nvidia-smi command. To monitor GPU usage, run:
```
watch -n 1 nvidia-smi
```
This will update the GPU usage every second without cluttering the terminal environment. Press Ctrl+C to exit.

Notes

This is my first foray into setting up a server and ever working with Linux so there may be better ways to do some of the steps. I will update this repository as I learn more.
I chose Debian because it is, apparently, one of the most stable Linux distros. I also went with an XFCE desktop environment because it is lightweight and I wasn't yet comfortable going full command line.
The power draw of my EVGA FTW3 Ultra RTX 3090 was 350W at stock settings. I set the power limit to 250W and the performance decrease was negligible for my use case, which is primarily code completion in VS Code and the Q&A via chat.
Use a user for auto-login, don't log in as root unless for a specific reason.
If something using a Docker container doesn't work, try running sudo docker ps -a to see if the container is running. If it isn't, try running sudo docker compose up -d again. If it is and isn't working, try running sudo docker restart (container_ID) to restart the container.
If something isn't working no matter what you do, try rebooting the server. It's a common solution to many problems. Try this before spending hours troubleshooting. Sigh.

References

Downloading Nvidia drivers:

https://wiki.debian.org/NvidiaGraphicsDrivers

Downloading AMD drivers:

https://wiki.debian.org/AtiHowTo

Secure Boot:

https://askubuntu.com/a/927470

Monitoring GPU usage, power draw:

https://unix.stackexchange.com/questions/38560/gpu-usage-monitoring-cuda/78203#78203

Passwordless sudo:

Auto-login:

Expose Ollama to LAN:

Firewall:

https://www.digitalocean.com/community/tutorials/how-to-set-up-a-firewall-with-ufw-on-debian-10

Passwordless ssh:

https://www.raspberrypi.com/documentation/computers/remote-access.html#configure-ssh-without-a-password

Adding CUDA to PATH:

https://askubuntu.com/questions/885610/nvcc-version-command-says-nvcc-is-not-installed

Docs:

Acknowledgements

Cheers to all the fantastic work done by the open-source community. This guide wouldn't exist without the effort of the many contributors to the projects and guides referenced here.

Please star any projects you find useful and consider contributing to them if you can. Stars on this guide would also be appreciated if you found it helpful, as it helps others find it too.

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Local LLaMA Server Setup Documentation

Table of Contents

About

System Requirements

Prerequisites

Essential Setup

Update the system

Install drivers

Nvidia

AMD

Install ollama

Create the init.bash script

Add init.bash to the crontab

Give nvidia-persistenced and nvidia-smi passwordless sudo permissions

Configure auto-login

Additional Setup

SSH

Firewall

Open WebUI

Docker

Open WebUI

OpenedAI Speech

Using Piper

Using XTTS

Open WebUI Integration

Downloading Voices

Accessing Ollama

Troubleshooting

Nvidia drivers

ollama

ssh

Open WebUI

OpenedAI Speech

Monitoring

Notes

References

Acknowledgements

About

Topics

Resources

Stars

Watchers

Forks

Releases

Packages 0

Install `ollama`

Create the `init.bash` script

Add `init.bash` to the crontab

Give `nvidia-persistenced` and `nvidia-smi` passwordless `sudo` permissions

`ollama`

`ssh`

Packages