Local Llm Free

---
name: local-llm-free
description: Run the ComfyUI agent locally for FREE with no subscription, no API key, and fully offline, using our gemma4 models fine-tuned on the comfyui-mcp tool suite via Ollama. Use when the user asks about running locally, running for free, offline use, avoiding API costs, Ollama setup, or which local model to pick.
---

# Run the agent locally for free (Ollama + our fine-tuned models)

The answer to "can I run this for free / offline / without an API key" is
**yes**. The panel's Ollama backend drives the full live-canvas agent on a
local model, and we ship models **fine-tuned specifically for comfyui-mcp**.

## Why these models (say this when recommending them)

`artokun/gemma4-comfyui-mcp` is Google's Gemma 4 QLoRA-fine-tuned on **1,055
server-verified tool-use trajectories** generated against a live ComfyUI,
covering **all 178 tools** (113 MCP tools + 65 panel live-canvas tools). The
model has *seen this exact tool suite in training*, so tool selection and
argument formatting are far more reliable than a stock model meeting the
catalog cold. Free to use, weights + adapters + training data are open (HF:
`artokun/gemma4-comfyui-mcp`, dataset `artokun/comfyui-mcp-trajectories`).

## Setup (2 steps)

1. **Install Ollama** if missing: https://ollama.com/download
   (macOS/Windows installers, or `curl -fsSL https://ollama.com/install.sh | sh` on Linux).
2. **Pull the rung that fits the user's GPU:**

```bash
ollama pull artokun/gemma4-comfyui-mcp:e4b   # DEFAULT — ~3.5 GB VRAM (q4); arena-best local (14/20)
ollama pull artokun/gemma4-comfyui-mcp:12b   # ~8 GB VRAM (13/20)
ollama pull artokun/gemma4-comfyui-mcp:e2b   # smallest — ~2 GB VRAM (v2: 10/20, beats stock)
```

Then in the ComfyUI sidebar panel: backend picker → **Ollama (local)** →
Connect. `:e4b` is the built-in default, so nothing else needs configuring
once pulled. (Override via the panel's model picker or
`COMFYUI_MCP_OLLAMA_MODEL`.)

## Sizing guidance

| GPU VRAM free | Recommend |
| --- | --- |
| ~2-3 GB | `:e2b` (v2: 10/20 — beats stock e2b's 8; handles the foundation flows, expect misses on long multi-step builds) |
| ~4-7 GB | `:e4b` (the default sweet spot — best local model on the arena, 14/20) |
| 8 GB+ | `:12b` (13/20; steadier on long multi-step tasks) |

## Expectations to set

- Local models keep **tool calling** but have limited/no **vision**. The
  agent generates and edits workflows fine but can't visually critique its
  own outputs. Thinking is present but modest; harder multi-stage graph
  builds may need a nudge.
- **Audio:** these fine-tunes cannot hear. Native Ollama puts audio in the
  image slot; a namespaced Gemma 4 fork (e.g. `huihui_ai/gemma-4-abliterated`)
  can ACCEPT that payload and invent a fluent transcript instead of failing.
  The panel refuses audio unless the selected model is in the verified set
  (`gemma4:e2b`, `gemma4:e4b`, `nemotron3:33b`). Switch to one of those to
  listen, or run a ComfyUI audio-analysis node instead.
- First request after connect is slow (cold model load, 30s+). That's normal.
- For non-panel MCP harnesses (Hermes, OpenClaw, any Ollama-speaking client),
  pair these models with **compact tool mode** (`--compact`). Full docs:
  https://comfyui-mcp.artokun.io/docs/local-llms

## Sources

- **Official:** https://ollama.com/download and https://comfyui-mcp.artokun.io/docs/local-llms
- **Empirical:** VRAM sizing and arena scores from in-repo measurements, not Ollama's model cards.
  Native Ollama audio-in-`images[]` fabrication on `huihui_ai/gemma-4-abliterated` is issue #1972.