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 requisite drivers, setting the GPU power limit, 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`](https://github.com/ggerganov/llama.cpp) or [LM Studio](https://lmstudio.ai) instead of Ollama or vLLM.
- **Simplicity of setup process**: It should be relatively straightforward to set up the components of the solution.
- **Stability of runtime**: The components should be stable and capable of running for weeks at a time without any intervention necessary.
- **Ease of maintenance**: The components and their interactions should be uncomplicated enough that you know enough to maintain them as they evolve (because they *will* evolve).
- **Aesthetics**: The result should be as close to a cloud provider's chat platform as possible. A homelab solution doesn't necessarily need to feel like it was cobbled together haphazardly.
- **Open source**: The code should be able to be verified by a community of engineers. Chat platforms and LLMs involve large amounts of personal data conveyed in natural language and it's important to know that data isn't going outside your machine.
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](https://ollama.com/blog/amd-preview).
> **AMD GPUs**: Power limiting is skipped for AMD GPUs as [AMD has recently made it difficult to set power limits on their GPUs](https://www.reddit.com/r/linux_gaming/comments/1b6l1tz/no_more_power_limiting_for_amd_gpus_because_it_is/). Naturally, skip any steps involving `nvidia-smi` or `nvidia-persistenced` and the power limit in the `init.bash` script.
>
> **CPU-only**: You can skip the GPU driver installation and power limiting steps. The rest of the guide should work as expected.
For a more detailed guide on installing Debian, refer to the [official documentation](https://www.debian.org/releases/buster/amd64/). 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.
Docker is a containerization platform that allows you to run applications in isolated environments. This subsection follows [this guide](https://docs.docker.com/engine/install/debian/) to install Docker Engine on Debian.
> This prerequisite only applies to those who want to use vLLM as an inference engine.
- Create a new virtual environment:
```
python3 -m venv hf-env
source hf-env/bin/activate
```
- Download the `huggingface_hub[cli]` package using `pip`:
```
pip install -U "huggingface_hub[cli]"
```
- Log in to HF Hub:
```
huggingface-cli login
```
- Enter your token when prompted.
- Run the following to verify your login:
```
huggingface-cli whoami
```
The result should be your username.
> [!TIP]
> If you intend to install vLLM via Python (manually) instead of Docker, and you don't intend to use HuggingFace with any other backend aside from vLLM, I'd recommend using the same virtual environment for `huggingface_hub` and `vllm`, say `vllm-env`.
- Follow Nvidia's [guide on downloading CUDA Toolkit](https://developer.nvidia.com/cuda-downloads?target_os=Linux&target_arch=x86_64&Distribution=Debian). The instructions are specific to your machine and the website will lead you to them interactively.
In this step, we'll create a script called `init.bash`. 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.
> 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.
### Configuring Script Permissions
We want `init.bash` to run the `nvidia-smi` commands without having to enter a password. This is done by giving `nvidia-persistenced` and `nvidia-smi` passwordless `sudo` permissions, and can be achieved by editing the `sudoers` file.
AMD users can skip this step as power limiting is not supported on AMD GPUs.
> 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.
## 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.
## Inference Engine
The inference engine is one of the primary components of this setup. It is code that takes model files containing weights and makes it possible to get useful outputs from them. This guide allows a choice between Ollama and vLLM - both are mature, production-grade inference engines with different priorities and stengths.
> If you installed Ollama manually or don't use it as a service, remember to run `ollama serve` to properly start the server. Refer to [Ollama's troubleshooting steps](#ollama-3) if you encounter an error.
### vLLM
vLLM comes with its own OpenAI-compatible API that we can use just like Ollama. Where Ollama runs GGUF model files, vLLM can run AWQ, GPTQ, GGUF, BitsAndBytes, and safetensors (the default release type) natively.
> [!NOTE]
> By default, vLLM uses HuggingFace's model download destination (`~/.cache/huggingface/hub`). Adding and removing models is easiest done via the HuggingFace CLI.
> Replace `<your_hf_hub_token>` with your HuggingFace Hub token and `<model>` with your desired model tag, copied from HuggingFace.
**Manual Installation**
- Create and activate a new virtual environment for vLLM's packages:
```
python3 -m venv vllm-env
source vllm-env/bin/activate
```
- Install vLLM using `pip`:
```
pip install vllm
```
- Serve vLLM with your desired flags. It uses port 8000 by default, but I'm using port 8556 here so it doesn't conflict with any other services:
```
vllm serve <model> --port 8556
```
- To use as a service, add the following block to `init.bash` to serve vLLM on startup:
```
source vllm-env/bin/activate
vllm serve <model> --port 8556
```
> Replace `<model>` with your desired model tag, copied from HuggingFace.
#### Serving a Different Model
Assuming you served the vLLM OpenAI-compatible API via Docker:
- First stop the existing container:
```
sudo docker ps -a
sudo docker stop <vllm_container_ID>
```
- If you want to run the exact same setup again in the future, skip this step. Otherwise, run the following to delete the container and not clutter your Docker container environment:
```
sudo docker rm <vllm_container_ID>
```
- Rerun the Docker command from the installation with the desired model.
The reference for vLLM's engine arguments (context length, quantization type, etc.) can be found [here](https://docs.vllm.ai/en/v0.6.1/models/engine_args.html).
Running the Docker command with a model name should download and then load your desired model. However, there are times where you may want to download from HuggingFace manually and delete old models to free up space.
First, activate the virtual environment that contains `huggingface_hub`:
```
source hf-env/bin/activate
```
Models are downloaded using their HuggingFace tag. Here, we'll use Qwen/Qwen2-VL-2B-Instruct as an example. To download a model, run:
This will start an interactive session where you can remove models from the HuggingFace directory. In case you've been saving models in a different location than `.cache/huggingface`, deleting models from there will free up space but the metadata will remain in the HF cache until it is deleted properly. This can be done via the above command but you can also simply delete the model directory from `.cache/huggingface/hub`.
You can find the HuggingFace CLI documentation [here](https://huggingface.co/docs/huggingface_hub/main/en/guides/cli).
#### Open WebUI Integration
Navigate to `Admin Panel > Settings > Connections` and set the following values:
- Enable OpenAI API
- API Base URL: `http://host.docker.internal:8556/v1`
- **Supports vision models**: vLLM already supports vision LMs like Qwen 2.5 VL and LLaMA 3.2. This feature is not supported by `llama.cpp`, but Ollama has separately implemented support (only for `llama3.2-vision` as of Jan 12, 2025).
- **Faster GPU inference**: Using GGUFs vs. native safetensors leaves performance on the table. vLLM is also better for distributed inference across multiple GPUs.
- **Broader compatibility**: vLLM can run model quantization types (AWQ, GPTQ, BnB, etc.) aside from just GGUF.
You can always keep both and decide to spin them up at your discretion. Using Ollama as a service offers no degradation in experience because unused models are offloaded from VRAM after some time. Using vLLM as a service keeps a model in memory, so I wouldn't use this alongside Ollama in an automated, always-on fashion unless it was your primary inference engine. Essentially,
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.
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.
> `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.
> [!NOTE]
> 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.
OpenedAI Speech is a text-to-speech server that wraps [Piper TTS](https://github.com/rhasspy/piper) and [Coqui XTTS v2](https://docs.coqui.ai/en/latest/models/xtts.html) 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.
> As of v0.17 (compared to v0.10), OpenedAI Speech features a far more straightforward and automated Docker installation, making it easy to get up and running.
Piper TTS is a lightweight 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:
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.
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.
- 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 voice:
```
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:
Kokoro FastAPI is a text-to-speech server that wraps around and provides OpenAI-compatible API inference for [Kokoro-82M](https://huggingface.co/hexgrad/Kokoro-82M), a state-of-the-art TTS model. The documentation for this project is fantastic and covers most, if not all, of the use cases for the project itself.
The server can be used in two ways: an API and a UI. By default, the API is served on port 8880 and the UI is served on port 7860.
#### Open WebUI Integration
Navigate to `Admin Panel > Settings > Audio` and set the following values:
- Text-to-Speech Engine: `OpenAI`
- API Base URL: `http://host.docker.internal:8880/v1`
- API Key: `anything-you-like`
- Set Model: `kokoro`
- Response Splitting: None (this is crucial - Kokoro uses a novel audio splitting system)
### Comparison
You may choose OpenedAI Speech over Kokoro because:
1) **Voice Cloning**: xTTS v2 offers extensive support for cloning voices with small samples of audio.
2) **Choice of Voices**: Piper offers a very large variety of voices across multiple languages, dialects, and accents.
You may choose Kokoro over OpenedAI Speech because:
1) Natural Tone: Kokoro's voices are very natural sounding and offer a better experience than Piper. While Piper has high quality voices, the text can sound robotic when reading out complex words/sentences.
2) Advanced Splitting: Kokoro splits responses up in a better format, making any pauses in speech feel more real. It also natively skips over Markdown formatting like lists and asterisks for bold/italics.
Kokoro's performance makes it an ideal candidate for regular use as a voice assistant chained to a language model in Open WebUI.
ComfyUI is a popular open-source graph-based tool for generating images using image generation models such as Stable Diffusion XL, Stable Diffusion 3, and the Flux family of models.
Read the installation instructions from [ComfyUI's GitHub](https://github.com/comfyanonymous/ComfyUI?tab=readme-ov-file#intel-gpus).
- Download the general dependencies:
```
pip install -r requirements.txt
```
Now, we have to download and load a model. Here, we'll use FLUX.1 [dev], a new, state-of-the-art medium-tier model by Black Forest Labs that fits well on an RTX 3090 24GB. Since we want this to be set up as easily as possible, we'll use a complete checkpoint that can be loaded directly into ComfyUI. For a completely customized workflow, CLIPs, VAEs, and models can be downloaded separately. Follow [this guide](https://comfyanonymous.github.io/ComfyUI_examples/flux/#simple-to-use-fp8-checkpoint-version) by ComfyUI's creator to install the FLUX.1 models in a fully customizable way.
> [!NOTE]
> [FLUX.1 [schnell] HuggingFace](https://huggingface.co/Comfy-Org/flux1-schnell/blob/main/flux1-schnell-fp8.safetensors) (smaller, ideal for <24GB VRAM)
>
> [FLUX.1 [dev] HuggingFace](https://huggingface.co/Comfy-Org/flux1-dev/blob/main/flux1-dev-fp8.safetensors) (larger, ideal for 24GB VRAM)
- Download your desired model into `/models/checkpoints`.
- If you want ComfyUI to be served at boot and effectively run as a service, add the following lines to `init.bash`:
```
cd /path/to/comfyui
source comfyui/bin/activate
python main.py --listen
```
> Replace `/path/to/comfyui` with the correct relative path to `init.bash`.
Otherwise, to run it just once, simply execute the above lines in a terminal window.
#### Open WebUI Integration
Navigate to `Admin Panel > Settings > Images` and set the following values:
- Image Generation Engine: `ComfyUI`
- API Base URL: `http://localhost:8188`
> [!TIP]
> You'll either need more than 24GB of VRAM or to use a small language model mostly on CPU to use Open WebUI with FLUX.1 [dev]. FLUX.1 [schnell] and a small language model, however, should fit cleanly in 24GB of VRAM, making for a faster experience if you intend to regularly use both text and image generation together.
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.
> [!NOTE]
> If you expect to tunnel into your server often, I highly recommend following [this guide](https://www.raspberrypi.com/documentation/computers/remote-access.html#configure-ssh-without-a-password) 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.
Here, we're allowing SSH (port 22), HTTPS (port 443), Open WebUI (port 3000), Ollama (port 11434), vLLM (port 8556), HTTP (port 80), OpenedAI Speech (8000), Docker (port 8080), and ComfyUI (port 8188). You can add or remove ports as needed.
- Enable UFW:
```
sudo ufw enable
```
- Check the status of UFW:
```
sudo ufw status
```
> [!WARNING]
> Enabling UFW without allowing access to port 22 will disrupt your existing SSH connections. If you run a headless setup, this means connecting a monitor to your server and then allowing SSH access through UFW. Be careful to ensure that this port is allowed when making changes to UFW's configuration.
Refer to [this guide](https://www.digitalocean.com/community/tutorials/how-to-set-up-a-firewall-with-ufw-on-debian-10) for more information on setting up UFW.
Remote access refers to the ability to access your server outside of your home network. For example, when you leave the house, you aren't going to be able to access `http://(your_server_ip)`, because your network has changed from your home network to some other network (either your mobile carrier's or a local network in some other place). This means that you won't be able to access the services running on your server. There are many solutions on the web that solve this problem and we'll explore some of the easiest-to-use here.
### Tailscale
Tailscale is a peer-to-peer VPN service that combines many services into one. Its most common use-case is to bind many different devices of many different kinds (Windows, Linux, macOS, iOS, Android, etc.) on one virtual network. This way, all these devices can be connected to different networks but still be able to communicate with each other as if they were all on the same local network. Tailscale is not completely open source (its GUI is proprietary), but it is based on the [Wireguard](https://www.wireguard.com) VPN protocol and the remainder of the actual service is open source. Comprehensive documentation on the service can be found [here](https://tailscale.com/kb) and goes into many topics not mentioned here - I would recommend reading it to get the most of out the service.
On Tailscale, networks are referred to as tailnets. Creating and managing tailnets requires creating an account with Tailscale (an expected scenario with a VPN service) but connections are peer-to-peer and happen without any routing to Tailscale servers. This connection being based on Wireguard means 100% of your traffic is encrypted and cannot be accessed by anyone but the devices on your tailnet.
#### Installation
First, create a tailnet through the Admin Console on Tailscale. Download the Tailscale app on any client you want to access your tailnet from. For Windows, macOS, iOS, and Android, the apps can be found on their respective OS app stores. After signing in, your device will be added to the tailnet.
For Linux, the steps required are as follows.
1) Install Tailscale
```
curl -fsSL https://tailscale.com/install.sh | sh
```
2) Start the service
```
sudo tailscale up
```
For SSH, run `sudo tailscale up --ssh`.
#### Exit Nodes
An exit node allows access to a different network while still being on your tailnet. For example, you can use this to allow a server on your network to act as a tunnel for other devices. This way, you can not only access that device (by virtue of your tailnet) but also all the devices on the host network its on. This is useful to access non-Tailscale devices on a network.
To advertise a device on as an exit node, run `sudo tailscale up --advertise-exit-node`. To allow access to the local network via this device, add the `--exit-node-allow-lan-access` flag.
#### Local DNS
If one of the devices on your tailnet runs a [DNS-sinkhole](https://en.wikipedia.org/wiki/DNS_sinkhole) service like [Pi-hole](https://pi-hole.net), you'll probably want other devices to use it as their DNS server. Assume this device is named `poplar`. This means every networking request made by a any device on your tailnet will send this request to `poplar`, which will in turn decide whether that request will be answered or rejected according to your Pi-hole configuration. However, since `poplar` is also one of the devices on your tailnet, it will send networking requests to itself in accordance with this rule and not to somewhere that will actually resolve the request. Thus, we don't want such devices to accept the DNS settings according to the tailnet but follow their otherwise preconfigured rules.
To reject the tailnet's DNS settings, run `sudo tailscale up --accept-dns=false`.
#### Third-Party VPN Integration
Tailscale offers a [Mullvad VPN](https://mullvad.net/en) exit node add-on with their service. This add-on allows for a traditional VPN experience that will route your requests through a proxy server in some other location, effectively masking your IP and allowing the circumvention of geolocation restrictions on web services. Assigned devices can be configured from the Admin Console. Mullvad VPN has [proven their no-log policy](https://mullvad.net/en/blog/2023/4/20/mullvad-vpn-was-subject-to-a-search-warrant-customer-data-not-compromised) and offers a fixed $5/month price no matter what duration you choose to pay for.
To use a Mullvad exit on one of your devices, first find the exit node you want to use by running `sudo tailscale exit-node list`. Note the IP and run `sudo tailscale up --exit-node=<your_chosen_exit_node_ip>`.
> [!WARNING]
> Ensure the device is allowed to use the Mullvad add-on through the Admin Console first.
This section isn't strictly necessary by any means - if you use all the elements in the guide, a good experience in Open WebUI means you've succeeded with the goal of the guide. However, it can be helpful to test the disparate installations at different stages in this process.
Since the OLLAMA_HOST environment variable is set to 0.0.0.0, it's easy to access Ollama from anywhere on the network. To do so, simply update the `localhost` reference in your URL or command to match the IP address of your server.
If you see the `speech.mp3` file in the OpenedAI Speech directory, you should be good to go. If you're paranoid like I am, test it using a player like `aplay`. Run the following commands (from the OpenedAI Speech directory):
Updating your system is a good idea to keep software running optimally and with the latest security patches. Updates to Ollama allow for inference from new model architectures and updates to Open WebUI enable new features like voice calling, function calling, pipelines, and more.
I've compiled steps to update these "primary function" installations in a standalone section because I think it'd be easier to come back to one section instead of hunting for update instructions in multiple subsections.
### General
Upgrade Debian packages by running the following commands:
Follow Nvidia's guide [here](https://developer.nvidia.com/cuda-downloads?target_os=Linux&target_arch=x86_64&Distribution=Debian) to install the latest CUDA drivers.
> Don't skip this step. Not installing the latest drivers after upgrading Debian packages will throw your installations out of sync, leading to broken functionality. When updating, target everything important at once. Also, rebooting after this step is a good idea to ensure that your system is operating as expected after upgrading these crucial drivers.
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.
- 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.
- 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](https://askubuntu.com/a/927470) 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.
- If you run into `docker: Error response from daemon: unknown or invalid runtime name: nvidia.`, you probably have `--runtime nvidia` in your Docker statement. This is meant for `nvidia-docker`, [which is deprecated now](https://stackoverflow.com/questions/52865988/nvidia-docker-unknown-runtime-specified-nvidia). Removing this flag from your command should get rid of this error.
- If you receive the `could not connect to ollama app, is it running?` error, your Ollama instance wasn't served properly. This could be because of a manual installation or the desire to use it at-will and not as a service. To run the Ollama server once, run:
For detailed instructions on _manually_ configuring Ollama to run as a service (to run automatically at boot), read the official documentation [here](https://github.com/ollama/ollama/blob/main/docs/linux.md). You shouldn't need to do this unless your system faces restrictions using Ollama's automated installer.
- 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.
- If you still can't connect to your API endpoint, check your firewall settings. [This guide to UFW (Uncomplicated Firewall) on Debian](https://www.digitalocean.com/community/tutorials/how-to-set-up-a-firewall-with-ufw-on-debian-10) is a good resource.
- If you encounter ```RuntimeError: An error occurred while downloading using `hf_transfer`. Consider disabling HF_HUB_ENABLE_HF_TRANSFER for better error handling.```, add `HF_HUB_ENABLE_HF_TRANSFER=0` to the `--env` flag after your HuggingFace Hub token. If this still doesn't fix the issue -
- Ensure your user has all the requisite permissions for HuggingFace to be able to write to the cache. To give read+write access over the HF cache to your user (and, thus, `huggingface-cli`), run:
```
sudo chmod 777 ~/.cache/huggingface
sudo chmod 777 ~/.cache/huggingface/hub
```
- Manually download a model via the HuggingFace CLI and specify `--download-dir=~/.cache/huggingface/hub` in the engine arguments. If your `.cache/huggingface` directory is being troublesome, specify another directory to the `--download-dir` in the engine arguments and remember to do the same with the `--local-dir` flag in any `huggingface-cli` commands.
- 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.
- 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](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html). 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:
> 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:
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.
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.
- 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.
- 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.
- Using a power monitor, I measured the power draw of my server for multiple days - the running average is ~60W. The power can spike to 350W during prompt processing and token generation, but this only lasts for a few seconds. For the remainder of the generation time, it tended to stay at the 250W power limit and dropped back to the average power draw after the model wasn't in use for about 20 seconds.
- Ensure your power supply has enough headroom for transient spikes (particularly in multi GPU setups) or you may face random shutdowns. Your GPU can blow past its rated power draw and also any software limit you set for it based on the chip's actual draw. I usually aim for +50% of my setup's estimated total power draw.
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.
To stay up-to-date on the latest developments in the field of machine learning, LLMs, and other vision/speech models, check out [r/LocalLLaMA](https://reddit.com/localllama).
> [!NOTE]
> 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.
