inferhost 0.5.3

Run any Hugging Face GGUF model on your own GPU — TUI only. Type `inferhost` and you're done.

inferhost

📖 Full documentation: https://amirrouh.github.io/inferhost/

Run any Hugging Face GGUF model on your own machine — TUI only. inferhost is a small Python framework that wraps llama.cpp and llama-swap behind a single LiteLLM gateway, exposing one OpenAI-compatible endpoint at http://<host>:9001/v1. llama-swap is now internal and binds loopback only.

Key features:

• Single endpoint, always on: LiteLLM is bundled (no extra required) and auto-starts on :9001.
• TurboQuant asymmetric KV cache compression: K=q8_0, V=turbo3 by default (the TurboQuant authors' recommended pairing — "V is free, K is everything"). ~3-4× total KV reduction via a custom llama-server built from TheTom/llama-cpp-turboquant.
• Pin = load now, with VRAM guard: Pressing P immediately loads the model into VRAM. If it won't fit, inferhost warns you and asks you to unpin something else first.
• Prebuilt binaries, nothing to compile: inferhost ships llama-server as prebuilt assets for Linux x86_64 CUDA, Linux x86_64 CPU, and macOS arm64 Metal.

uv tool install inferhost
inferhost

That's it. The first launch downloads the runtime binaries (llama-server + llama-swap) for you with a progress bar; then the dashboard opens and you can add, start, stop, and inspect models from the keyboard.

What it does

• One-key serving of any GGUF model published on Hugging Face.
• Automatic quantization selection based on available VRAM (Q6 → Q5 → Q4 → IQ4 fallback).
• OpenAI-compatible API out of the box, including tool calling and vision for any GGUF that ships an mmproj-*.gguf (auto-downloaded alongside the main file).
• Stacked speculative decoding for MTP-capable models — combines llama.cpp's --spec-type draft-mtp with --spec-type ngram-mod so MTP handles novel tokens while ngram-mod dominates on repeated patterns (code, function names, etc.).
• Multi-model support via llama-swap, which lazy-loads model backends on demand.
• Auto-detected hardware: NVIDIA CUDA, CPU, or Apple Silicon Metal (prebuilt assets); for Vulkan/ROCm, point INFERHOST_LLAMA_SERVER_PATH at your own binary.
• Live download progress for both runtime binaries and Hugging Face model files.
• Full control from the TUI — change ports, edit context size and GPU layers, watch status of every daemon. No editor, no YAML, no extra commands.
• All defaults still overridable through environment variables or a .env file — the TUI just writes another .env file at ~/.config/inferhost/inferhost.env so your changes survive restarts.

Installation

Requirements: Python 3.11+, Linux or macOS. NVIDIA CUDA, Linux CPU, or Apple Silicon Metal are the supported prebuilt targets.

inferhost is a CLI app, not a library — install it globally with uv tool (or pipx), not into a project's dependencies.

# Recommended — global, isolated, on your PATH
uv tool install inferhost

# Alternatives
pipx install inferhost
pip install inferhost            # only inside an existing venv

Note: In v0.4 and earlier, LiteLLM was an optional [gateway] extra (inferhost[gateway]). From v0.5 it is bundled — a plain uv tool install inferhost is all you need. The [gateway] extra still exists as an empty alias for one release to avoid breaking existing install scripts.

⚠️ Don't use uv add inferhost. uv add pins it as a project dependency, so you can only run it via uv run inferhost inside that one project directory. Use uv tool install so inferhost is a normal command on your PATH.

Upgrade

uv tool upgrade inferhost                # if installed with `uv tool`
pipx upgrade inferhost                   # if installed with pipx
pip install -U inferhost                 # if installed with pip

To pin a specific version:

uv tool install --force 'inferhost==0.5.0'

Uninstall

uv tool uninstall inferhost              # if installed with `uv tool`
pipx uninstall inferhost                 # if installed with pipx
pip uninstall inferhost                  # if installed with pip

Inferhost stores runtime binaries, logs, and the model registry outside the Python install. To wipe everything (binaries, llama-server logs, model registry — but not downloaded GGUFs, which live in the Hugging Face cache):

rm -rf ~/.local/share/inferhost ~/.config/inferhost

To also drop downloaded models: rm -rf ~/.cache/huggingface/hub/models--*.

Usage

There is exactly one command:

inferhost

This opens the TUI. On first launch it downloads llama-server and llama-swap with a progress bar. Afterward you land on the dashboard.

Keys

Key	Action
a	Add a Hugging Face model (downloads the GGUF + any mmproj-*.gguf for vision)
n	Rename the highlighted model's public alias (regenerates llama-swap + LiteLLM configs)
c	Configure the highlighted model: per-model context window (-c)
P	Toggle pin on the highlighted model — pins load the model into VRAM immediately; unpinning unloads it. inferhost checks VRAM first and warns if it won't fit.
d / Delete	Remove the highlighted model from the registry
s	Start llama-swap
x	Stop llama-swap
r	Restart llama-swap
p	Open the Settings panel (ports, context, GPU layers, flash attention)
R	Refresh
q	Quit

The top of the dashboard shows two live status rows: a GPU bar (per-card VRAM bar, used / total, utilization — via nvidia-smi, hidden on non-NVIDIA boxes) and a status bar with the daemon dots, ports, the selected model's active ctx, and which model llama-swap currently has resident in VRAM (loaded: <name>). Both refresh every two seconds.

Adding a model

Press a, type a Hugging Face repo id (e.g. Qwen/Qwen2.5-7B-Instruct-GGUF), and press Enter. The TUI lists the available GGUF files, marks the recommended quant for your hardware, and shows a live progress bar while it downloads. The model is registered against llama-swap and ready to serve.

Pinning models (load into VRAM immediately)

Press P on a highlighted model to pin it. Pinning loads the model into VRAM right away — it does not wait for the next request. inferhost checks available VRAM before pinning; if the model won't fit, a modal appears asking you to unpin something else first. Press P again on a pinned model to unpin and unload it. The sidebar marks pinned models with a ★.

Renaming a model

The name shown in the model list is also the value clients send as the OpenAI model field. Press n to change it. inferhost rewrites the llama-swap and LiteLLM configs in one shot and, if llama-swap is running, restarts it so the new alias is reachable immediately. No need to edit any YAML by hand.

Changing ports and other settings

Press p to open the Settings panel. You can edit swap_port, gateway_port, default_ctx, gpu_layers, and flash_attention directly. Saving writes a managed env file at ~/.config/inferhost/inferhost.env, so your changes persist across restarts. Press r afterwards to restart llama-swap with the new values.

Endpoint

The single OpenAI-compatible endpoint is the LiteLLM gateway on port 9001:

curl http://localhost:9001/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen2.5-7b-instruct-q4-k-m",
    "messages": [{"role": "user", "content": "Hello"}]
  }'

Use the model name column from the dashboard as the model field.

Configuration

Every setting is overridable through environment variables or a .env file in the working directory. Copy .env.example for the full list.

Variable	Default	Purpose
INFERHOST_SWAP_PORT	9090	llama-swap listen port (internal, loopback-only in v0.5+).
INFERHOST_GATEWAY_PORT	9001	LiteLLM gateway port — the user-facing OpenAI endpoint.
INFERHOST_KV_QUANT_K	q8_0	K cache type (-ctk). Keep at q8_0 or f16; turbo on K is discouraged.
INFERHOST_KV_QUANT_V	turbo3	V cache type (-ctv). Recommended: turbo4 (light) / turbo3 (default) / turbo2 (heavy).
INFERHOST_LLAMA_SERVER_PATH	(auto)	Path to a custom llama-server binary (Vulkan/ROCm/local builds).
INFERHOST_DATA_DIR	~/.local/share/inferhost	Binaries, logs, and PID files.
INFERHOST_CONFIG_DIR	~/.config/inferhost	Model registry and generated YAML.
INFERHOST_HF_CACHE	~/.cache/huggingface	Hugging Face model cache.
INFERHOST_GPU_LAYERS	99	-ngl value passed to llama-server.
INFERHOST_DEFAULT_CTX	8192	Default context length for new models.
INFERHOST_FLASH_ATTENTION	on	-fa flag for llama-server.
INFERHOST_PARALLEL_SLOTS	1	--parallel flag — concurrent request slots per llama-server instance. 1 = serial.
INFERHOST_REASONING	auto	--reasoning flag — thinking mode for capable models. on, off, or auto.
INFERHOST_REASONING_BUDGET	-1	--reasoning-budget flag — token cap on thinking. -1 = unlimited, 0 = none.
INFERHOST_LLAMACPP_BACKEND	auto	Force a backend: vulkan, cuda, rocm, sycl, openvino, or cpu.
INFERHOST_LLAMACPP_VERSION	latest	Pin a specific llama.cpp release tag.
INFERHOST_LLAMASWAP_VERSION	latest	Pin a specific llama-swap release tag.
INFERHOST_SPEC_DRAFT_N_MAX	2	MTP draft tokens per step (only used on MTP-capable models). Set to 0 to disable the MTP lane.
INFERHOST_SPEC_NGRAM_MOD_N_MATCH	24	Minimum matching sequence length before ngram-mod drafts.
INFERHOST_SPEC_NGRAM_MOD_N_MIN	48	Minimum context window ngram-mod searches back through.
INFERHOST_SPEC_NGRAM_MOD_N_MAX	64	Max draft tokens ngram-mod proposes on a strong match.

Architecture

   Client                inferhost
   ------                ---------
   Your app  --HTTP-->   LiteLLM gateway        llama-swap (loopback)   llama-server
                         :9001 (public)  --->   127.0.0.1:9090   --->   (llama.cpp)

• llama.cpp runs the inference via a prebuilt TurboQuant-enabled llama-server (Linux x86_64 CUDA, Linux x86_64 CPU, macOS arm64 Metal).
• llama-swap sits in front of multiple llama-server instances and lazy-loads them on demand. It binds loopback (127.0.0.1) only.
• LiteLLM is the single user-facing gateway — always on, always bundled, serving :9001.

Troubleshooting: If you try curl http://<lan-ip>:9090/... and it fails, that is expected — llama-swap is loopback-only by design in v0.5+. Use the LiteLLM port :9001 instead.

Development

The repo ships a run.sh wrapper for source-tree work:

git clone [e-mail]:amirrouh/inferhost.git
cd inferhost
./run.sh install     # creates venv, installs in editable mode
./run.sh start       # launches the TUI (downloads binaries on first run)
./run.sh status      # headless status print
./run.sh stop        # stop daemons
./run.sh test        # run pytest

Run ./run.sh help for the full list. End users do not need run.sh — they only ever type inferhost.

License

Apache 2.0