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# Self Hosted AI Stack
- [Self Hosted AI Stack](#self-hosted-ai-stack)
- [Notes](#notes)
- [Podman Volume Locations](#podman-volume-locations)
- [Setup](#setup)
- [Create the AI user](#create-the-ai-user)
- [Helper aliases](#helper-aliases)
- [Create the models dir](#create-the-models-dir)
- [Install the Hugging Face CLI](#install-the-hugging-face-cli)
- [Samba Model Storage](#samba-model-storage)
- [Download models](#download-models)
- [Text models](#text-models)
- [GPT-OSS](#gpt-oss)
- [Mistral](#mistral)
- [Qwen](#qwen)
- [GLM](#glm)
- [Gemma](#gemma)
- [Dolphin](#dolphin)
- [Image models](#image-models)
- [Z-Image](#z-image)
- [Flux](#flux)
- [Embedding Models](#embedding-models)
- [Qwen Embedding](#qwen-embedding)
- [Nomic Embedding](#nomic-embedding)
- [llama.cpp](#llamacpp)
- [stable-diffusion.cpp](#stable-diffusioncpp)
- [open-webui](#open-webui)
- [Install Services with Quadlets](#install-services-with-quadlets)
- [Internal and External Pods](#internal-and-external-pods)
- [Llama CPP Server](#llama-cpp-server)
- [Llama CPP Embedding Server](#llama-cpp-embedding-server)
- [Stable Diffusion CPP](#stable-diffusion-cpp)
- [Open Webui](#open-webui-1)
- [Install the update script](#install-the-update-script)
- [Install Guest Open Webui with Start/Stop Services](#install-guest-open-webui-with-startstop-services)
- [Benchmark Results](#benchmark-results)
## Notes
### Podman Volume Locations
`~/.local/share/containers/storage/volumes/`
## Setup
### Create the AI user
```bash
# Create your local ai user. This will be the user you launch podman processes from.
useradd -m ai
loginctl enable-linger ai
su -l ai
mkdir -p /home/ai/.config/containers/systemd/
mkdir -p /home/ai/.ssh
```
Models are big. You'll want some tools to help find large files quickly when space runs out.
### Helper aliases
Add these to your .bashrc:
```bash
# Calculate all folder sizes in current dir
alias {dudir,dud}='du -h --max-depth 1 | sort -h'
# Calculate all file sizes in current dir
alias {dufile,duf}='ls -lhSr'
# Restart llama-server / follow logs
alias llama-reload="systemctl --user daemon-reload && systemctl --user restart llama-server.service"
alias llama-logs="journalctl --user -fu llama-server"
# Restart stable diffusion gen and edit server / follow logs
alias sd-gen-reload='systemctl --user daemon-reload && systemctl --user restart stable-diffusion-gen-server'
alias sd-gen-logs='journalctl --user -xeu stable-diffusion-gen-server'
alias sd-edit-reload='systemctl --user daemon-reload && systemctl --user restart stable-diffusion-edit-server'
alias sd-edit-logs='journalctl --user -xeu stable-diffusion-edit-server'
```
### Create the models dir
```bash
mkdir -p /home/ai/models/{text,image,video,embedding,tts,stt}
```
### Install the Hugging Face CLI
<https://huggingface.co/docs/huggingface_hub/en/guides/cli#getting-started>
```bash
# Install
curl -LsSf https://hf.co/cli/install.sh | bash
# Login
hf auth login
```
### Samba Model Storage
I recommend adding network storage for keeping models offloaded. This mounts a samba share at `/srv/models`.
```bash
dnf install -y cifs-utils
# Add this to /etc/fstab
//driveripper.reeselink.com/smb_models /srv/models cifs _netdev,nofail,uid=1001,gid=1001,credentials=/etc/samba/credentials 0 0
# Then mount
systemctl daemon-reload
mount -a --mkdir
```
Here are some sync commands that I use to keep the samba share in sync with the home directory:
```bash
# Sync models from home dir to the samba share
rsync -av --progress /home/ai/models/ /srv/models/
```
### Download models
In general I try to run 8 bit quantized minimum.
#### Text models
<https://huggingface.co/ggml-org/collections>
##### GPT-OSS
<https://unsloth.ai/docs/models/gpt-oss-how-to-run-and-fine-tune#recommended-settings>
```bash
# gpt-oss-120b
mkdir gpt-oss-120b && cd gpt-oss-120b
hf download --local-dir . ggml-org/gpt-oss-120b-GGUF
# gpt-oss-20b
mkdir gpt-oss-20b && cd gpt-oss-20b
hf download --local-dir . ggml-org/gpt-oss-20b-GGUF
```
##### Mistral
```bash
# devstral-small-2-24b
mkdir devstral-small-2-24b && cd devstral-small-2-24b
hf download --local-dir . ggml-org/Devstral-Small-2-24B-Instruct-2512-GGUF Devstral-Small-2-24B-Instruct-2512-Q8_0.gguf
# ministral-3-14b
mkdir ministral-3-14b && cd ministral-3-14b
hf download --local-dir . ggml-org/Ministral-3-14B-Reasoning-2512-GGUF
# ministral-3-3b-instruct
mkdir ministral-3-3b-instruct && cd ministral-3-3b-instruct
hf download --local-dir . ggml-org/Ministral-3-3B-Instruct-2512-GGUF
```
##### Qwen
```bash
# qwen3-30b-a3b-thinking
mkdir qwen3-30b-a3b-thinking && cd qwen3-30b-a3b-thinking
hf download --local-dir . ggml-org/Qwen3-30B-A3B-Thinking-2507-Q8_0-GGUF
# qwen3-30b-a3b-instruct
mkdir qwen3-30b-a3b-instruct && cd qwen3-30b-a3b-instruct
hf download --local-dir . ggml-org/Qwen3-30B-A3B-Instruct-2507-Q8_0-GGUF
# qwen3-vl-30b-a3b-thinking
mkdir qwen3-vl-30b-a3b-thinking && cd qwen3-vl-30b-a3b-thinking
hf download --local-dir . Qwen/Qwen3-VL-30B-A3B-Thinking-GGUF Qwen3VL-30B-A3B-Thinking-Q8_0.gguf
hf download --local-dir . Qwen/Qwen3-VL-30B-A3B-Thinking-GGUF mmproj-Qwen3VL-30B-A3B-Thinking-F16.gguf
# qwen3-vl-30b-a3b-instruct
mkdir qwen3-vl-30b-a3b-instruct && cd qwen3-vl-30b-a3b-instruct
hf download --local-dir . Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF Qwen3VL-30B-A3B-Instruct-Q8_0.gguf
hf download --local-dir . Qwen/Qwen3-VL-30B-A3B-Instruct-GGUF mmproj-Qwen3VL-30B-A3B-Instruct-F16.gguf
# qwen3-coder-30b-a3b-instruct
mkdir qwen3-coder-30b-a3b-instruct && cd qwen3-coder-30b-a3b-instruct
hf download --local-dir . ggml-org/Qwen3-Coder-30B-A3B-Instruct-Q8_0-GGUF
# qwen3-coder-next
mkdir qwen3-coder-next && cd qwen3-coder-next
hf download --local-dir . unsloth/Qwen3-Coder-Next-GGUF --include "Q8_0/*.gguf"
```
##### GLM
```bash
# glm-4.7-flash-30b
mkdir glm-4.7-flash-30b && cd glm-4.7-flash-30b
hf download --local-dir . unsloth/GLM-4.7-Flash-GGUF GLM-4.7-Flash-Q8_0.gguf
```
##### Gemma
```bash
# Note "it" vs "pt" suffixes. "it" is instruction following, "pt" is the base model (not as good for out-of-the-box use)
# gemma-3-27b-it
mkdir gemma-3-27b-it && cd gemma-3-27b-it
hf download --local-dir . unsloth/gemma-3-27b-it-GGUF gemma-3-27b-it-Q8_0.gguf
hf download --local-dir . unsloth/gemma-3-27b-it-GGUF mmproj-F16.gguf
```
##### Dolphin
```bash
# dolphin-mistral-24b-venice
mkdir dolphin-mistral-24b-venice && cd dolphin-mistral-24b-venice
cd dolphin-mistral-24b-venice
hf download --local-dir . bartowski/cognitivecomputations_Dolphin-Mistral-24B-Venice-Edition-GGUF cognitivecomputations_Dolphin-Mistral-24B-Venice-Edition-Q8_0.gguf
```
#### Image models
##### Z-Image
```bash
# z-turbo
# Fastest image generation in 8 steps. Great a text and prompt following.
# Lacks variety.
mkdir /home/ai/models/image/z-turbo && cd /home/ai/models/image/z-turbo
hf download --local-dir . leejet/Z-Image-Turbo-GGUF z_image_turbo-Q8_0.gguf
hf download --local-dir . black-forest-labs/FLUX.1-schnell ae.safetensors
hf download --local-dir . unsloth/Qwen3-4B-Instruct-2507-GGUF Qwen3-4B-Instruct-2507-Q8_0.gguf
```
##### Flux
```bash
# flux2-klein
# Capable of editing images in 4 steps (though 5 is my recommended steps)
mkdir /home/ai/models/image/flux2-klein && cd /home/ai/models/image/flux2-klein
hf download --local-dir . leejet/FLUX.2-klein-9B-GGUF flux-2-klein-9b-Q8_0.gguf
hf download --local-dir . black-forest-labs/FLUX.2-dev ae.safetensors
hf download --local-dir . unsloth/Qwen3-8B-GGUF Qwen3-8B-Q8_0.gguf
```
#### Embedding Models
##### Qwen Embedding
```bash
mkdir /home/ai/models/embedding/qwen3-vl-embed && cd /home/ai/models/embedding/qwen3-vl-embed
hf download --local-dir . dam2452/Qwen3-VL-Embedding-8B-GGUF Qwen3-VL-Embedding-8B-Q8_0.gguf
```
##### Nomic Embedding
```bash
# nomic-embed-text-v2
mkdir /home/ai/models/embedding/nomic-embed-text-v2
hf download --local-dir /home/ai/models/embedding/nomic-embed-text-v2 ggml-org/Nomic-Embed-Text-V2-GGUF
```
## llama.cpp
<https://github.com/ggml-org/llama.cpp/tree/master/tools/server>
```bash
# Build the llama.cpp container image
git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
export BUILD_TAG=$(date +"%Y-%m-%d-%H-%M-%S")
# Vulkan (better performance as of Feb 2026)
podman build -f .devops/vulkan.Dockerfile -t llama-cpp-vulkan:${BUILD_TAG} -t llama-cpp-vulkan:latest .
# ROCM
podman build -f .devops/rocm.Dockerfile -t llama-cpp-rocm:${BUILD_TAG} -t llama-cpp-rocm:latest .
# Run llama demo server (Available on port 8000)
podman run \
--rm \
--name llama-server-demo \
--device=/dev/kfd \
--device=/dev/dri \
-v /home/ai/models/text:/models:z \
-p 8000:8000 \
localhost/llama-cpp-vulkan:latest \
--host 0.0.0.0 \
--port 8000 \
-c 32768 \
--perf \
--n-gpu-layers all \
--jinja \
--models-max 1 \
--models-dir /models
```
## stable-diffusion.cpp
Server: <https://github.com/leejet/stable-diffusion.cpp/tree/master/examples/server>
CLI: <https://github.com/leejet/stable-diffusion.cpp/tree/master/examples/cli>
```bash
git clone https://github.com/leejet/stable-diffusion.cpp.git
cd stable-diffusion.cpp
git submodule update --init --recursive
export BUILD_TAG=$(date +"%Y-%m-%d-%H-%M-%S")
# Vulkan
podman build -f Dockerfile.vulkan -t stable-diffusion-cpp:${BUILD_TAG} -t stable-diffusion-cpp:latest .
```
```bash
# Generate an image with z-turbo
podman run --rm \
-v /home/ai/models:/models:z \
-v /home/ai/output:/output:z \
--device /dev/kfd \
--device /dev/dri \
localhost/stable-diffusion-cpp:latest \
--diffusion-model /models/image/z-turbo/z_image_turbo-Q8_0.gguf \
--vae /models/image/z-turbo/ae.safetensors \
--llm /models/image/z-turbo/Qwen3-4B-Instruct-2507-Q8_0.gguf \
-v \
--cfg-scale 1.0 \
--vae-conv-direct \
--diffusion-conv-direct \
--fa \
--mmap \
--seed -1 \
--steps 8 \
-H 1024 \
-W 1024 \
-o /output/output.png \
-p "A photorealistic dragon"
# Edit the generated image with flux2-klein
podman run --rm \
-v /home/ai/models:/models:z \
-v /home/ai/output:/output:z \
--device /dev/kfd \
--device /dev/dri \
localhost/stable-diffusion-cpp:latest \
--diffusion-model /models/image/flux2-klein/flux-2-klein-9b-Q8_0.gguf \
--vae /models/image/flux2-klein/ae.safetensors \
--llm /models/image/flux2-klein/Qwen3-8B-Q8_0.gguf \
-v \
--cfg-scale 1.0 \
--sampling-method euler \
--vae-conv-direct \
--diffusion-conv-direct \
--fa \
--mmap \
--steps 5 \
-H 1024 \
-W 1024 \
-r /output/output.png \
-o /output/edit.png \
-p "Replace the dragon with an old car"
```
## open-webui
```bash
mkdir /home/ai/.env
# Create a file called open-webui-env with `WEBUI_SECRET_KEY="some-random-key"
scp active/software_ai_stack/secrets/open-webui-env deskwork-ai:.env/
# Will be available on port 8080
podman run \
-d \
-p 8080:8080 \
-v open-webui:/app/backend/data \
--name open-webui \
--restart always \
ghcr.io/open-webui/open-webui:main
```
Use the following connections:
| Service | Endpoint |
| ------------------------- | ----------------------------------------- |
| llama.cpp server | <http://host.containers.internal:8000> |
| llama.cpp embed | <http://host.containers.internal:8001> |
| stable-diffusion.cpp | <http://host.containers.internal:1234/v1> |
| stable-diffusion.cpp edit | <http://host.containers.internal:1235/v1> |
## Install Services with Quadlets
### Internal and External Pods
These will be used to restrict internet access to our llama.cpp and
stable-diffusion.cpp services while allowing the frontend services to
communicate with those containers.
```bash
scp -r active/software_ai_stack/quadlets_pods/* deskwork-ai:.config/containers/systemd/
ssh deskwork-ai
systemctl --user daemon-reload
systemctl --user start ai-internal-pod.service ai-external-pod.service
```
### Llama CPP Server
Installs the llama.cpp server to run our text models.
```bash
scp -r active/software_ai_stack/quadlets_llama_server/* deskwork-ai:.config/containers/systemd/
ssh deskwork-ai
systemctl --user daemon-reload
systemctl --user restart ai-internal-pod.service
```
### Llama CPP Embedding Server
Installs the llama.cpp server to run our embedding models
```bash
scp -r active/software_ai_stack/quadlets_llama_embed/* deskwork-ai:.config/containers/systemd/
ssh deskwork-ai
systemctl --user daemon-reload
systemctl --user restart ai-internal-pod.service
```
### Stable Diffusion CPP
Installs the stable-diffusion.cpp server to run our image models.
```bash
scp -r active/software_ai_stack/quadlets_stable_diffusion/* deskwork-ai:.config/containers/systemd/
ssh deskwork-ai
systemctl --user daemon-reload
systemctl --user restart ai-internal-pod.service
```
### Open Webui
Installs the open webui frontend.
```bash
scp -r active/software_ai_stack/quadlets_openwebui/* deskwork-ai:.config/containers/systemd/
ssh deskwork-ai
systemctl --user daemon-reload
systemctl --user restart ai-external-pod.service
```
Note, all services will be available at `host.containers.internal`. So llama.cpp
will be up at `http://host.containers.internal:8000`.
### Install the update script
```bash
# 1. Builds the latest llama.cpp and stable-diffusion.cpp
# 2. Pulls the latest open-webui
# 3. Restarts all services
scp active/software_ai_stack/update-script.sh deskwork-ai:
ssh deskwork-ai
chmod +x update-script.sh
./update-script.sh
```
### Install Guest Open Webui with Start/Stop Services
Optionally install a guest openwebui service.
```bash
scp -r active/software_ai_stack/systemd/. deskwork-ai:.config/systemd/user/
ssh deskwork-ai
systemctl --user daemon-reload
systemctl --user enable open-webui-guest-start.timer
systemctl --user enable open-webui-guest-stop.timer
```
## Benchmark Results
Benchmarks are run with [unsloth gpt-oss-20b Q8_0](https://huggingface.co/unsloth/gpt-oss-20b-GGUF/blob/main/gpt-oss-20b-Q8_0.gguf)
```bash
# Run the llama.cpp pod (AMD)
podman run -it --rm \
--device=/dev/kfd \
--device=/dev/dri \
-v /home/ai/models/text:/models:z \
--entrypoint /bin/bash \
ghcr.io/ggml-org/llama.cpp:full-vulkan
# Benchmark command
./llama-bench -m /models/benchmark/gpt-oss-20b-Q8_0.gguf
```
Framework Desktop
| model | size | params | backend | ngl | test | t/s |
| ---------------- | --------: | ------: | ------- | ---: | ----: | -------------: |
| gpt-oss 20B Q8_0 | 11.27 GiB | 20.91 B | Vulkan | 99 | pp512 | 1128.50 ± 7.60 |
| gpt-oss 20B Q8_0 | 11.27 GiB | 20.91 B | Vulkan | 99 | tg128 | 77.94 ± 0.08 |
| model | size | params | backend | ngl | test | t/s |
| ---------------- | --------: | ------: | ------- | ---: | ----: | ------------: |
| gpt-oss 20B Q8_0 | 11.27 GiB | 20.91 B | ROCm | 99 | pp512 | 526.05 ± 7.04 |
| gpt-oss 20B Q8_0 | 11.27 GiB | 20.91 B | ROCm | 99 | tg128 | 70.98 ± 0.01 |
AMD R9700
| model | size | params | backend | ngl | test | t/s |
| ---------------- | --------: | ------: | ------- | ---: | ----: | ---------------: |
| gpt-oss 20B Q8_0 | 11.27 GiB | 20.91 B | Vulkan | 99 | pp512 | 3756.79 ± 203.97 |
| gpt-oss 20B Q8_0 | 11.27 GiB | 20.91 B | Vulkan | 99 | tg128 | 174.24 ± 0.32 |
NVIDIA GeForce RTX 4080 SUPER
| model | size | params | backend | ngl | test | t/s |
| ---------------- | --------: | ------: | ------- | ---: | ----: | ------------: |
| gpt-oss 20B Q8_0 | 11.27 GiB | 20.91 B | CUDA | 99 | tg128 | 193.28 ± 1.03 |
| gpt-oss 20B Q8_0 | 11.27 GiB | 20.91 B | CUDA | 99 | tg256 | 193.55 ± 0.34 |
| gpt-oss 20B Q8_0 | 11.27 GiB | 20.91 B | CUDA | 99 | tg512 | 187.39 ± 0.10 |
NVIDIA GeForce RTX 3090
| model | size | params | backend | ngl | test | t/s |
| ---------------- | --------: | ------: | ------- | ---: | ----: | --------------: |
| gpt-oss 20B Q8_0 | 11.27 GiB | 20.91 B | CUDA | 99 | pp512 | 4297.72 ± 35.60 |
| gpt-oss 20B Q8_0 | 11.27 GiB | 20.91 B | CUDA | 99 | tg128 | 197.73 ± 0.62 |
Apple M4 max
| model | test | t/s |
| :---------------------------- | -----: | -------------: |
| unsloth/gpt-oss-20b-Q8_0-GGUF | pp2048 | 1579.12 ± 7.12 |
| unsloth/gpt-oss-20b-Q8_0-GGUF | tg32 | 113.00 ± 2.81 |
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