Run Gemma 4 with OpenClaw in Three Simple Steps – Official Google Guide
This article walks through Google’s official three‑step tutorial for connecting the Gemma 4 language model to OpenClaw using Ollama, details hardware requirements, discusses performance and security considerations, and evaluates the model’s capabilities compared to larger LLMs.
Running Gemma 4 on iPhone
When the context length grows or deep‑thinking mode is enabled, response speed drops, the device heats up and battery drains quickly. These symptoms were reported in the comment thread of the article “iPhone 本地跑 Gemma 4 火了，0 token 时代还有多远？”
Alternative workflow: Gemma 4 + OpenClaw on a computer
Users have experimented with installing Gemma 4 on a desktop (e.g., a Mac Studio) and connecting it to OpenClaw (named “龙虾”). One blogger claimed that a Mac Studio running the 31B version could recoup token‑cost savings within three months, contingent on model performance.
Official three‑step tutorial (Google Gemma team)
Download and install ollama from https://ollama.com/download.
Obtain a Gemma 4 checkpoint. Google recommends the 26B A4B checkpoint, but the step can be omitted because step 3 handles the download.
Launch OpenClaw with Gemma 4 as the backend via Ollama. Ollama automatically installs OpenClaw and starts it with the 26B A4B model.
Hardware requirements
For the official 26B A4B checkpoint, a Mac Studio with an M4 Pro CPU and 48 GB RAM (or an equivalent machine) is suggested.
GPU‑equipped laptops need at least 18 GB VRAM.
A Mac Studio with 48 GB RAM can run the model with as little as 16 GB VRAM.
Performance and limitations
Even the largest Gemma 4 version lags behind top‑tier models such as Opus in reasoning ability, tool usage, and handling long contexts. Users note that the workflow is simple—no hidden dependencies, endpoint configuration, or missing embeddings—but the model’s intelligence ceiling remains a concern.
Security considerations
Peter Steinberger, creator of OpenClaw, warned that cheap or low‑capacity local models are more vulnerable to prompt‑injection attacks, whereas more capable models can better detect such threats.
Overall assessment
The three‑step process removes many of the manual steps previously required for local agent setups, but practitioners must weigh the cost‑free deployment against the model’s reduced reasoning power and potential security risks.
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