inferhost 0.4.8

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, llama-swap, and (optionally) LiteLLM behind a single Textual TUI. Point it at a Hugging Face repository and it returns an OpenAI-compatible endpoint.

pip 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 via Vulkan, AMD via ROCm, Intel via SYCL/OpenVINO, or CPU.
• 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, set a per-model context window, rename a model's public alias, toggle the LiteLLM gateway, 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, AMD, Intel, or Apple Silicon GPUs are auto-detected; CPU-only is supported.

# Recommended
uv tool install inferhost

# Or with pip
pip install inferhost

# With the LiteLLM gateway (unified endpoint + routing + aliases)
pip install 'inferhost[gateway]'

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) and KV cache quant (-ctk/-ctv)
d / Delete	Remove the highlighted model from the registry
s	Start llama-swap
x	Stop llama-swap
r	Restart llama-swap
g	Toggle the LiteLLM gateway on/off
p	Open the Settings panel (ports, context, GPU layers, flash attention)
R	Refresh
q	Quit

The top of the dashboard always shows the running state of both the llama-swap and the (optional) litellm daemon, plus a one-line summary of every setting currently in effect.

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.

Configuring a model (per-model context + KV cache quant)

The global default_ctx (in Settings) applies to newly added models, but you can override it per-model. Highlight a model and press c to open the per-model settings panel, where you can edit:

• Context window (-c) — tokens per request for this model.
• KV cache type K / V (-ctk / -ctv) — quantization of the KV cache. Leave blank for llama.cpp's default (f16). q8_0 is near-lossless and roughly halves KV memory; q4_0 cuts it ~4× but starts to bite on long contexts. The smallest near-lossless way to fit a larger ctx into the same VRAM is -ctk q8_0 -ctv q8_0.

inferhost saves the values to the registry, re-renders llama-swap.yaml, and reloads any running daemon so the new flags take effect immediately.

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 dashboard shows the current OpenAI-compatible endpoint, e.g. http://localhost:9090/v1. Use the model name column in any OpenAI client:

curl http://localhost:9090/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "qwen2.5-7b-instruct-q4-k-m",
    "messages": [{"role": "user", "content": "Hello"}]
  }'

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 (user-facing OpenAI endpoint).
INFERHOST_GATEWAY_PORT	9001	LiteLLM gateway port when enabled.
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_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. Set to 0 to disable the ngram-mod lane.

Architecture

   Client                inferhost                       Inference
   ------                ---------                       ---------
   Your app  --HTTP-->   llama-swap        spawns/kills  llama-server
                         :9090                           (llama.cpp)
                            ^
                            |
                  (optional) LiteLLM
                         :9001

• llama.cpp runs the inference (using a prebuilt Vulkan, CUDA, ROCm, SYCL, OpenVINO, or CPU binary, whichever fits the host).
• llama-swap sits in front of multiple llama-server instances and lazy-loads them on demand.
• LiteLLM (optional) provides a unified gateway with friendly aliases, routing, rate limits, and fallbacks across local and hosted providers.

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