inferhost 0.7.9

Run any Hugging Face GGUF model on your own GPU — chat/vision, text-to-speech (OuteTTS), and image generation (SDXL/Flux/Z-Image/Qwen-Image via stable-diffusion.cpp), all behind one OpenAI-compatible endpoint. Pulls official upstream binaries, no compiling. Type `inferhost` and you're done.

🛰️ inferhost

Your own private, multi-modal AI server — one command, any GPU, no compiling.

Chat · Vision · Speech · Image generation — all behind one OpenAI-compatible endpoint.

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inferhost turns any GPU box into a private AI server. It wraps llama.cpp and stable-diffusion.cpp behind a single OpenAI-compatible endpoint — pulls the official upstream binaries for you (nothing to compile), auto-fetches the right model files when you paste a Hugging Face link, and hot-swaps models in and out of VRAM so one card can serve a big LLM and image generation. You only ever touch a keyboard-driven dashboard (and an optional .env).

⚡ Quick start

uv tool install inferhost      # or:  pipx install inferhost  /  pip install inferhost
inferhost                      # opens the dashboard — press 'a' to add a model

That's the whole setup. First launch fetches the runtime binaries automatically. To add a model, press a and paste a Hugging Face repo — inferhost lists the files, downloads what's needed, and serves it. Then call it like OpenAI:

🗣️ Chat / LLM	🔊 Text-to-speech	🎨 Image generation
paste Qwen/Qwen2.5-7B-Instruct-GGUF	paste OuteAI/OuteTTS-0.2-500M-GGUF	paste OlegSkutte/sdxl-turbo-GGUF

# 🗣️  Chat  →  /v1/chat/completions
curl http://localhost:9001/v1/chat/completions -H 'Content-Type: application/json' \
  -d '{"model":"<name-from-dashboard>","messages":[{"role":"user","content":"Hello!"}]}'

# 🔊  Speech  →  /v1/audio/speech   (returns WAV)
curl http://localhost:9001/v1/audio/speech -H 'Content-Type: application/json' \
  -d '{"model":"<name>","input":"Hello from inferhost.","voice":"default"}' --output speech.wav

# 🎨  Image  →  /v1/images/generations   (returns base64 PNG)
curl http://localhost:9001/v1/images/generations -H 'Content-Type: application/json' \
  -d '{"model":"<name>","prompt":"a red apple on a table","size":"512x512"}' \
  | jq -r '.data[0].b64_json' | base64 -d > out.png

Everything lives on http://localhost:9001/v1 — point any OpenAI client (Python SDK, Open WebUI, your app) at it. The model name is whatever shows in the dashboard.

✨ Why inferhost

• One endpoint, every modality — chat, vision, speech, and images on the same OpenAI-compatible :9001. No per-model servers to wire up.
• Nothing to compile — official llama-server / sd-server binaries are pulled from upstream for your hardware (NVIDIA Vulkan, ROCm, SYCL, CPU, Apple Metal).
• Paste a link, it figures out the rest — picks the best quant for your VRAM, and for multi-file models (Flux, Z-Image, Qwen-Image) auto-downloads the right VAE + text encoders from known-good repos.
• One GPU, many models — llama-swap lazy-loads and hot-swaps models in/out of VRAM on demand, so a 24 GB card serves a 27B LLM and Flux image generation.
• TUI or headless — drive everything from a keyboard dashboard, or run inferhost start/stop/status on a server with no terminal.
• Tuned by default — q8_0 KV-cache compression, stacked MTP + ngram speculative decoding, and honest context windows, all overridable from a .env.

🧩 Supported models

Modality	Models	How
Chat / Vision	any GGUF LLM (Qwen, Llama, Gemma, DeepSeek…), vision via mmproj	paste repo → pick quant
Speech (TTS)	OuteTTS	paste repo (vocoder auto-detected)
Image — single-file	SD 1.5, SDXL (incl. Turbo)	paste repo → pick file
Image — Flux.1	schnell / dev	auto-fetches VAE + CLIP-L + T5XXL
Image — Flux.2 Klein	incl. Bonsai-Image (1-bit)	auto-fetches VAE + Qwen3-4B
Image — Z-Image	Z-Image-Turbo	auto-fetches VAE + Qwen3-4B
Image — Qwen-Image	Qwen-Image / Qwen-Image-Edit	auto-fetches VAE + Qwen2.5-VL + mmproj

All image families above were verified end-to-end on a Vulkan GPU (SDXL-Turbo ~2 s, Flux-schnell ~4 s, Bonsai ~2 s, Z-Image-Turbo ~11 s, Qwen-Image-Edit via /v1/images/edits).

📚 Documentation

Full guides live in docs (and in the docs/ folder):

• Installation — install, upgrade, uninstall, requirements
• Usage — the dashboard, keyboard keys, and chat / TTS / image / Flux / Z-Image / Qwen-Image walkthroughs
• Configuration — every .env variable, KV-cache quant, custom binaries
• Troubleshooting — ports, tmux mouse, common errors

🏗️ Architecture

Your app ──HTTP──▶  LiteLLM gateway        llama-swap (loopback)       llama-server  (chat/vision)
                    :9001 (public)   ──▶    127.0.0.1:9090      ──┬──▶  sd-server     (images)
                                                                  └──▶  inferhost-tts (speech)

• llama.cpp (llama-server) runs chat/vision inference — official upstream binary, backend auto-detected.
• llama-swap fronts the model backends and lazy-loads / hot-swaps them on demand (loopback only). Image models (sd-server) ride here too, so they swap VRAM with LLMs.
• inferhost-tts wraps llama.cpp's llama-tts behind /v1/audio/speech (started only when a TTS model is registered).
• LiteLLM is the single always-on public gateway on :9001, routing each request to the right backend.

The extra engines (llama-tts, sd-server) are fetched automatically the first time you add a model that needs them.

🛠️ Development

The repo ships a run.sh wrapper for source-tree work (end users never need it — they only type inferhost):

git clone [e-mail]:amirrouh/inferhost.git && cd inferhost
./run.sh install     # venv + editable install
./run.sh start       # launch the TUI (downloads binaries on first run)
./run.sh status      # headless status
./run.sh stop        # stop daemons
./run.sh test        # pytest

Run ./run.sh help for the full list.

License

Apache 2.0