Unlock Private AI on Mac Studio 128GB: One‑Click Multi‑Model Deployment & Auto‑Switch

Running a single large model on a top‑spec Mac Studio 128GB wastes memory; the unified 128 GB RAM allows loading 2‑3 mainstream models simultaneously without out‑of‑memory errors.

The guide uses the March 2026 Arena AI leaderboard to pick five high‑scoring open‑source models (Qwen3.5 Plus, Gemma 4 26B, Qwen3.5 9B, CodeQwen 7B, DeepSeek‑R1 7B) covering complex reasoning, everyday tasks, code, and math.

First, install Ollama, the simplest local model manager for macOS, by running: curl -fsSL https://ollama.com/install.sh | sh Then pull the selected models with one‑click commands:

ollama pull qwen:9b
ollama pull gemma4:26b
ollama pull qwen3.5
ollama pull codeqwen
ollama pull deepseek-r1

OpenClaw is configured to route queries to the appropriate model automatically. The provided openclaw.yaml sets a primary model (Gemma 4 26B) and fallbacks, and defines routing rules for simple tasks, code tasks, math tasks, and complex long‑text tasks.

# OpenClaw multi‑model auto‑routing (Mac 128GB)

gateway:
  host: 127.0.0.1
  port: 18789
agents:
  defaults:
    model:
      primary: "ollama/gemma4:26b"
      fallbacks:
        - "ollama/qwen:9b"
        - "ollama/qwen3.5"
        - "ollama/codeqwen"
        - "ollama/deepseek-r1"
  models:
    "ollama/qwen:9b":
      alias: "lightweight"
      max_tokens: 4096
    "ollama/gemma4:26b":
      alias: "general"
      max_tokens: 8192
    "ollama/qwen3.5":
      alias: "flagship"
      max_tokens: 16384
    "ollama/codeqwen":
      alias: "code"
      max_tokens: 8192
    "ollama/deepseek-r1":
      alias: "math"
      max_tokens: 8192
providers:
  ollama:
    type: ollama
    base_url: "http://localhost:11434"
routing:
  enabled: true
  rules:
    - name: simple_task
      match:
        keywords: [总结, 提取, 翻译, 格式, JSON, 清单, 短句, 分类, 改写, 校对]
      model: "ollama/qwen:9b"
    - name: code_task
      match:
        keywords: [代码, 函数, debug, 算法, SQL, 前端, 后端, vue, react, java, python, 编程]
      model: "ollama/codeqwen"
    - name: math_task
      match:
        keywords: [数学, 计算, 公式, 概率, 推导, 解题, 物理, 逻辑]
      model: "ollama/deepseek-r1"
    - name: complex_task
      match:
        keywords: [长文, 小说, 论文, 方案, 架构, 复杂推理, 深度思考, 多轮, 策划, 分析]
      model: "ollama/qwen3.5"
default_model: "ollama/gemma4:26b"

After saving the file, restart OpenClaw with openclaw restart. The system now automatically selects the optimal model: lightweight Qwen3.5 9B for quick summarisation, CodeQwen for programming, DeepSeek‑R1 for math, Gemma 4 for general chat, and Qwen3.5 Plus for demanding tasks.

Additional tips: view the current model with /model, manually switch with /model model_name, list installed models via ollama list, and monitor memory – all five models together use about 50 GB, leaving ample RAM.

In summary, the Mac Studio 128 GB equipped with this multi‑model, auto‑routing setup delivers fully private, zero‑API‑cost AI that adapts to task complexity without manual intervention.