What Can You Run on a Mac Studio M3 Ultra? OpenClaw Cost Savings Guide

Introduction

The Mac Studio M3 Ultra with 192GB of unified memory sits in a sweet spot for local AI inference. It's not cheap at $6,000+ for the 192GB configuration, but it might pay for itself if you're burning through API costs with OpenClaw or other AI agents.

Here's the brutal truth: Running an AI agent can cost $200-700+ per month in API fees, especially if you're using Claude Opus for main sessions and Sonnet for sub-agents. But what if you could cut that by 50-90% by running the right models locally?

This guide maps exactly which models fit, real-world performance numbers from the community, and how to configure OpenClaw to route the right tasks to local models while keeping complex reasoning on cloud APIs.

What Models Fit in 192GB Unified Memory

Let's start with the math. The Mac Studio M3 Ultra comes with up to 192GB of unified memory shared between CPU and GPU. Here's what actually fits:

Real Performance Benchmarks

Community benchmarks from r/LocalLLaMA, Hacker News, and YouTube show impressive performance on the M3 Ultra:

Runtime Comparison

Based on community benchmarks, here's how different runtimes perform on Mac Studio M3 Ultra:

• MLX direct: Fastest (230+ tok/s for small models, 33 tok/s for 70B)
• LM Studio (MLX): Nearly as fast, better UX (237 tok/s small, 33 tok/s 70B)
• Ollama: 20-30% slower but easiest API integration (149 tok/s small, 24 tok/s 70B)
• llama.cpp: Similar to Ollama, more manual setup required

OpenClaw Cost Mapping: The Big Picture

Here's where it gets interesting. Let's map OpenClaw's typical API usage to local alternatives and see the potential savings:

What to Keep on API vs Run Locally

Keep on API (For Now)

• Main agent complex reasoning: Opus still outperforms all local models for complex multi-step reasoning
• Coding tasks requiring deep context: GPT-4 and Claude maintain quality edge
• Critical business decisions: When accuracy matters more than cost

Move to Local

• Sub-agent tasks: Research, writing, summarization — 70B models handle these well
• Embeddings: Local models like nomic-embed match OpenAI quality
• Audio transcription/TTS: Whisper and local TTS models are nearly identical to APIs
• Image analysis: LLaVA can handle most vision tasks
• Code completion: CodeLlama matches GitHub Copilot for many tasks

OpenClaw Model Routing Configuration

Here's how to configure OpenClaw to intelligently route tasks between local and cloud models:

1. Set Up Ollama on Mac Studio

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Pull recommended models
ollama pull llama3.1:70b-instruct-q4_K_M     # Sub-agent tasks
ollama pull nomic-embed-text                   # Embeddings
ollama pull llava:13b                          # Vision tasks
ollama pull codellama:34b-instruct-q4_K_M     # Code assistance

2. Configure OpenClaw Model Routing

# ~/.openclaw/config/default.json5
{
  "agents": {
    "defaults": {
      "model": {
        "primary": "anthropic/claude-opus-4-6",    // Main agent stays on Opus
        "fallback": [
          "ollama/llama3.1:70b-instruct-q4_K_M",  // Fallback to local 70B
          "anthropic/claude-sonnet-4-20250514"     // Final fallback to Sonnet API
        ]
      }
    },
    "subagents": {
      "model": {
        "primary": "ollama/llama3.1:70b-instruct-q4_K_M",  // Sub-agents use local
        "fallback": ["anthropic/claude-sonnet-4-20250514"]  // API backup
      }
    }
  },
  "embeddings": {
    "provider": "ollama",
    "model": "nomic-embed-text"
  },
  "tts": {
    "provider": "local",  // Use local TTS
    "whisper": {
      "model": "large-v3",
      "local": true
    }
  }
}

3. Network Setup (If Mac Studio is Remote)

If your Mac Studio is on a different machine from your OpenClaw gateway:

# On Mac Studio - expose Ollama API
export OLLAMA_HOST=0.0.0.0:11434
ollama serve

# In OpenClaw config
{
  "providers": {
    "ollama": {
      "baseURL": "http://your-mac-studio-ip:11434/api"
    }
  }
}

4. Smart Task Routing Rules

Configure OpenClaw to route different task types automatically:

{
  "taskRouting": {
    "research": "ollama/llama3.1:70b-instruct-q4_K_M",
    "writing": "ollama/llama3.1:70b-instruct-q4_K_M", 
    "coding-simple": "ollama/codellama:34b-instruct-q4_K_M",
    "coding-complex": "anthropic/claude-sonnet-4-20250514",
    "reasoning": "anthropic/claude-opus-4-6",
    "vision": "ollama/llava:13b",
    "embeddings": "ollama/nomic-embed-text"
  }
}

Practical Considerations

Memory Management

• Ollama auto-unloads models after 5 minutes of inactivity to free memory
• Loading time: 70B models take 15-30 seconds to load initially
• Keep frequently used models loaded: Set longer timeout or use multiple smaller models
• Monitor memory usage: Use Activity Monitor to track model memory consumption

Performance Optimization

• Use Q4_K_M quantization for best quality/performance balance
• MLX > Ollama for pure speed but Ollama has better API compatibility
• Keep context reasonable: 8K-16K context is the sweet spot
• SSD matters: Fast storage helps with model loading

Latency Analysis

Quality Trade-offs

Clear Recommendations

For Heavy OpenClaw Users ($300+/month in APIs)

• Buy the Mac Studio M3 Ultra with 192GB — it'll pay for itself in 12-18 months
• Run Ollama with Llama 3.1 70B Q4 for sub-agents and research tasks
• Keep main agent on Claude Opus for complex reasoning
• Move all embeddings and audio to local — immediate 100% savings on those APIs
• Use local vision models for non-critical image analysis

For Moderate Users ($100-300/month in APIs)

• Consider the Mac Studio M2 Ultra or wait for used M3 units
• Start with embeddings and TTS local — easiest wins
• Test local 70B for sub-agents — quality is surprisingly good
• Keep complex tasks on API until you're comfortable with local quality

For Light Users ($50-100/month in APIs)

• Stick with APIs for now — hardware cost doesn't justify savings
• Consider a Mac Mini M4 Pro for embeddings and simple tasks
• Focus on better prompt engineering to reduce token usage

Specific Model Recommendations

Best Models for Each Task

• Sub-agent reasoning: Llama 3.3 70B or DeepSeek V3 67B (Q4)
• Code assistance: CodeLlama 34B or DeepSeek Coder 33B (Q4)
• Embeddings: nomic-embed-text (beats OpenAI text-embedding-3-small)
• Vision tasks: LLaVA 13B or Qwen3-VL (good enough for most tasks)
• Fast responses: Qwen 2.5 14B or Llama 3.1 8B for simple questions

Runtime Recommendation

Use Ollama for OpenClaw integration. While MLX is faster, Ollama's API compatibility and model management make it the practical choice for production agent workflows.

Getting Started: Implementation Steps

Week 1: Test the Waters

1. Install Ollama on your existing hardware
2. Pull a smaller model: ollama pull llama3.1:8b
3. Test quality against your typical sub-agent tasks
4. Measure current API costs with openclaw usage

Week 2-3: Scale Up Testing

1. Try larger models on available hardware: ollama pull llama3.1:70b-instruct-q4_K_M
2. Configure partial local routing for non-critical tasks
3. Monitor quality vs speed trade-offs
4. Test embeddings locally: ollama pull nomic-embed-text

Week 4: Make the Call

1. Calculate actual savings from your testing period
2. If savings > $200/month: Order Mac Studio M3 Ultra 192GB
3. If savings < $100/month: Stick with APIs and optimize prompts
4. In between? Consider Mac Mini M4 Pro or wait for M4 Ultra

Production Deployment

# Full production setup script
#!/bin/bash

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Pull production models
ollama pull llama3.1:70b-instruct-q4_K_M  # Main local model
ollama pull nomic-embed-text                # Embeddings
ollama pull llava:13b                       # Vision
ollama pull qwen2.5:14b-instruct-q4_K_M   # Fast responses

# Configure OpenClaw
openclaw config set agents.subagents.model.primary "ollama/llama3.1:70b-instruct-q4_K_M"
openclaw config set embeddings.provider "ollama"
openclaw config set embeddings.model "nomic-embed-text"

# Test the setup
ollama run llama3.1:70b-instruct-q4_K_M "Test message"
echo "Setup complete!"