Hugind runs open-weight LLMs on the hardware you already own. Chat with documents and images, automate work with agents, process anything an OpenAI-compatible tool can handle. No API keys. No per-token bills. Nothing leaves your office.
Chat you can use like ChatGPT. Agents you can trust with real work. Both running on your own hardware, both free.
Pull any open-weight model from Hugging Face and chat with it. Llama, Gemma, Qwen, Mistral, DeepSeek, vision-language models. Drop in an image and ask about it. See the model think through a problem before it answers. Plug it into anything that speaks OpenAI.
An agent reads your email, opens a browser, fills forms, runs a shell command, or writes a file. But only the things you explicitly grant. Safe automation of the boring parts of work, with a manifest your security team can actually review.
Ship a private assistant to every desk in the office without a monthly per-seat bill. Your team chats, summarizes, translates, codes, drafts, and works with images, all on one GPU server your IT team already runs. Nothing ever leaves the building.
A customer emails asking for a password reset. An agent watches the support inbox, reads the request, opens a browser, signs into the admin panel, resets the password, and replies with the new credentials. If anything looks unusual, it stops and tags a human. Every action is logged.
A government office processes hundreds of passports, court filings, or contracts a day. The documents legally cannot leave the jurisdiction. Hugind runs on a local GPU server: page-by-page OCR, structured extraction, redaction, and indexing. Same workflow as a cloud service, without the cross-border problem.
macOS, Linux, Windows. Models download from Hugging Face straight into your home directory. The server speaks OpenAI-compatible HTTP on port 8080.
# 1. Install the runtime. $ brew install hugind # 2. Pull a model from Hugging Face. $ hugind model add google/gemma-3-4b-it-qat-q4_0-gguf # 3. Create a hardware-aware config (auto-detects your GPU). $ hugind config init gemma-4b # 4. Start the OpenAI-compatible server. $ hugind server start gemma-4b ready on http://localhost:8080 # Talk to it like any OpenAI client. $ curl http://localhost:8080/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{"model":"gemma-4b", "messages":[{"role":"user","content":"who are you?"}]}'
You do not need a data center. Hugind auto-detects your hardware, picks the best backend, and tells you which models will fit before you download them. If the RAM is not there, it says so. If a smaller quant is a better fit, it suggests it.
7B-14B models on a MacBook. 70B on a Mac Studio with unified memory.
The gaming rig under your desk runs a useful model tonight.
Bigger models, more concurrent users, same binary, same config.
Industrial edge devices. A slow but working CPU path on everything else.
Ollama is easier for first contact. LM Studio has a nicer GUI. Raw llama.cpp gives you every low-level knob. Hugind is for teams that also want agents on top of inference.
| Hugind | Ollama | LM Studio | llama.cpp | |
|---|---|---|---|---|
| OpenAI-compatible server | Yes | Yes | Yes | Yes |
| Runs offline, no account, free | Yes | Yes | Yes | Yes |
| One-command install | Yes | Yes | Yes (GUI installer) | Build from source |
| Sandboxed agent runtime | Yes (JS + WASM) | No | No | No |
| Per-agent permission manifest | Yes | No | No | No |
| Multi-agent orchestration | Yes | No | No | No |
| MCP support | Yes | Experimental | No | No |
| Easiest first-time setup | Good | Best | Best (GUI) | Hardest |
| Low-level tuning knobs | Some | Some | Some | Best |
"Yes" means the feature exists in the default install and is documented. We update this table as competitors ship. Corrections welcome on GitHub.
Hugind is free to install, free to use, and free to redistribute. There is no paid tier, no trial, no upsell, no telemetry reporting home. The MIT license means you can also fork it, embed it, or ship it inside a product.
If you run Hugind in production and need help past the docs, imaged Lab does paid integration engineering and custom agents. That is the only commercial path, and it is kept entirely separate from the project.
