I kept running llama.cpp directly — building it, juggling llama-server flags, and hand-editing models.ini for every model. It's powerful but fiddly, so I built a GUI over it for myself and cleaned it up to share.

LlamaForge is a browser control panel that sits on top of llama.cpp's own router. It doesn't touch inference — llama.cpp does all the real work — it just makes driving it less painful.

What it does:

Tune every server parameter per model — the knobs are parsed live from llama-server --help (currently \~220), grouped and searchable. Save hot-reloads the model, no restart.

VRAM-fit model discovery — search HuggingFace for GGUFs and each quant is rated FITS / TIGHT / CPU OFFLOAD against your actual VRAM before you download.

Guided build & update — shows your current commit, how far behind upstream you are, and rebuilds with CMake flags auto-detected for your CPU/GPU (CUDA arch, AVX-512, etc.).

Sensible context defaults — reads each GGUF's trained context length and writes reasonable ctx-size values so models don't load with tiny or over-extended windows.

Setup tab — detects missing prereqs (CMake, Ninja, MSVC, CUDA…) and installs them via winget/choco with your permission, plus scans drives for existing GGUFs and prunes entries whose files you've deleted.

Usage stats + optional LAN sharing (with an API-key toggle) so other devices can hit the OpenAI-compatible endpoint.

Being upfront about scope:

Windows + NVIDIA focused right now (CPU-only builds work too).

You build llama.cpp yourself — it's guided from the dashboard, but it's still a compile step. If you want a zero-config, double-click experience, LM Studio / Ollama / Jan will serve you better; LlamaForge trades that for direct control over the real llama-server.

Early preview — expect rough edges, and I'd genuinely like the feedback.

Backend is pure-Python stdlib (nothing to pip install), MIT licensed, and not affiliated with ggml-org — all credit for the hard part goes to llama.cpp.

Repo: https://github.com/dadwritestech/LlamaForge

(Disclosure: I'm the author.) Happy to answer questions — especially curious whether the per-model flag editing and VRAM-fit ratings are useful to anyone else, or if I'm solving a problem only I have.

I built a GUI control panel for llama.cpp so I'd stop hand-editing models.ini and llama-server flags