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artokun / local-llm-free

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.

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SKILL.md
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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:
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 freeRecommend
~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.