Deploy Gemma 4 on OpenClaw: Run Google's New Open Model in 2026

Google released Gemma 4 and the search data tells the story — "gemma4 openclaw" is one of the fastest-rising OpenClaw queries this week. If you want to run Gemma 4 as the brain of your personal AI assistant, here is the full deploy path.

Why Gemma 4 + OpenClaw?

Gemma 4 is Google's latest open-weight model family. The smaller variants (2B, 9B) run on a laptop; the bigger ones (27B+) need a real GPU but deliver Claude-competitive quality on many tasks. Running it locally means no token bills and full data privacy.

OpenClaw is built for exactly this workflow: it speaks to any OpenAI-compatible endpoint, which both Ollama and LM Studio expose for local models. Point OpenClaw at your local Gemma 4 and your Telegram or Discord bot is suddenly running on your own hardware.

Option 1: Deploy via Ollama (Easiest)

Ollama is the simplest way to run Gemma 4 locally. Install it, pull the model, and it is running.

1. Install Ollama from ollama.com on the machine you want to run Gemma.
2. Run ollama pull gemma4:9b (or gemma4:27b if you have the VRAM).
3. Run ollama serve — it listens on http://localhost:11434 with an OpenAI-compatible API.

If your OpenClaw container runs on the same host, use http://host.docker.internal:11434/v1 as the base URL. If it is a separate machine, expose Ollama on your LAN and use that IP.

Option 2: Deploy via LM Studio

LM Studio offers a GUI, quantization options, and a one-click OpenAI-compatible server. It is great if you want to test multiple Gemma 4 quantizations before committing.

1. Download LM Studio, search "gemma 4", and download a quant that fits your GPU.
2. Load the model, open the Local Server tab, and click Start Server.
3. Copy the base URL — usually http://localhost:1234/v1.

Connecting Gemma 4 to OpenClaw

On OpenClaw Launch, open your instance config and set a custom provider:

• Provider name: local-gemma
• Base URL: http://host.docker.internal:11434/v1 (Ollama) or http://localhost:1234/v1 (LM Studio)
• API key: any non-empty string (local servers ignore it)
• Model ID: gemma4:9b or whatever Ollama/LM Studio reports

Save the config and redeploy. Your Telegram, Discord, or WhatsApp bot now answers using Gemma 4, with zero per-token cost.

Hardware Reality Check

• Gemma 4 2B — runs on a MacBook Air or any laptop with 8GB RAM. Fast, fine for basic chat.
• Gemma 4 9B — needs ~12GB VRAM or 16GB unified memory. Sweet spot for quality vs cost.
• Gemma 4 27B — needs a 24GB+ GPU (3090, 4090, M3 Max). Claude-competitive on many tasks.

When Local Gemma 4 Beats Cloud Models

Three scenarios where this setup shines:

• Privacy-critical work — the model never leaves your machine, ideal for legal, medical, or internal data.
• High-volume automation — if you are running thousands of messages a day, local inference saves real money.
• Offline or low-connectivity — local Gemma works on a boat, a flight, or a rural office.

Get Started

Spin up your own OpenClaw instance, connect it to a local Gemma 4 server, and you have a private, uncensored, multi-channel AI assistant running on your own hardware. Start at the OpenClaw Launch dashboard.