Mac Mini vs Mac Studio for Local AI

1. Why Run LLMs Locally?

Cloud APIs are powerful but expensive. If you use OpenClaw, Claude, or GPT-4 heavily, your monthly bill can easily hit $50–200+. Every heartbeat check, cron job, code completion, and research task burns tokens at Anthropic or OpenAI rates.

Running local models via Ollama on Apple Silicon lets you offload many of these tasks to hardware you own — with zero marginal cost per token, complete privacy, and no rate limits.

Apple Silicon is uniquely suited for this because of its unified memory architecture — the CPU and GPU share the same memory pool. This means a Mac Mini with 24GB of unified memory can use all 24GB for model weights, unlike a PC where your GPU's VRAM is separate from system RAM.

2. Why Memory Matters for LLMs

Unified Memory = Your Entire Pool Is Available

On a traditional PC, your RTX 4090 has 24GB of VRAM. If a model doesn't fit, it spills to system RAM over the PCIe bus — and performance collapses 10–20×. On Apple Silicon, there's no bus to cross. The GPU cores read directly from the same memory pool as the CPU.

Memory Bandwidth = Tokens Per Second

LLM inference during text generation is memory-bandwidth bound, not compute-bound. Every token generated requires reading the entire model's weights from memory. The formula is simple:

How Much Memory Do Models Need?

A rough rule of thumb for 4-bit quantized (Q4) models:

• ~0.5 GB per billion parameters for Q4_K_M quantization
• 7B model → ~4 GB
• 13B model → ~7.5 GB
• 33B model → ~19 GB
• 70B model → ~40 GB
• You also need 2–4 GB for the OS and KV cache overhead

Memory Bandwidth Comparison

While Apple Silicon can't match an RTX 4090's raw bandwidth, remember: the 4090 only has 24GB of VRAM. A Mac Studio with 128GB can run models the 4090 simply cannot fit.

3. The Mac Mini M4 Lineup (2024)

The Mac Mini was redesigned in late 2024 — it's now roughly the size of an Apple TV, starting at just $499. Here's every configuration relevant for local AI:

4. The Mac Studio Lineup (2025)

The Mac Studio was refreshed in March 2025 with M4 Max and M3 Ultra options. This is where serious local AI work happens — 36GB to 512GB of unified memory with massive bandwidth.

5. What Models Run on Which Machine?

Here's the practical guide — what you can actually run on each configuration, accounting for OS overhead and KV cache:

6. Real-World Benchmarks

These numbers come from community benchmarks running Ollama on actual Apple Silicon hardware:

M4 Max 128GB (MacBook Pro / Mac Studio equivalent)

From Reddit r/ollama benchmarks on a maxed-out M4 Max 128GB:

M4 Pro (Mac Mini)

From Sebastian Raschka's benchmarks, a Mac Mini M4 Pro achieves approximately 45 t/s on a 20B MoE model with mxfp4 precision — roughly matching NVIDIA's DGX Spark on the same task.

Scaling by Bandwidth

Since inference is bandwidth-bound, you can estimate performance across chips by scaling the M4 Max numbers:

7. OpenClaw Task Offloading — What Can Go Local?

Not every OpenClaw task needs Claude Opus or GPT-4. Here's a practical mapping of tasks to local model requirements:

8. Cost Savings Analysis

Typical API Costs

A moderate OpenClaw user running heartbeats, cron jobs, code assistance, and research might spend:

• Light use: $30–50/month (mostly heartbeats + simple tasks)
• Moderate use: $75–100/month (code completion + research)
• Heavy use: $150–300/month (complex reasoning + long context)

What Can You Offload?

Not everything goes local — Claude and GPT-4 still dominate for complex multi-step reasoning, very long context, and tasks requiring the latest knowledge. But 40–70% of typical usage can go local:

Electricity Is Negligible

Apple Silicon is incredibly power-efficient. A Mac Mini draws 5–7W at idle and 15–30W under LLM inference load. At $0.15/kWh average US electricity cost:

• Running 24/7 at full load: ~$3.50/month for a Mac Mini
• Running 24/7 at full load: ~$8/month for a Mac Studio
• Compare to an NVIDIA RTX 4090 rig: ~$35–50/month at full load

9. Setup Guide: Ollama on Mac

Step 1: Install Ollama

# Download from ollama.com or use Homebrew
brew install ollama

# Start the Ollama service
ollama serve

Step 2: Pull Your First Model

# For 16GB Mac — fast and capable
ollama pull llama3.1:8b

# For 24GB Mac — great for code
ollama pull qwen2.5:14b

# For 48GB Mac — strong reasoning
ollama pull qwen2.5:32b

# For 128GB Mac Studio — frontier-class
ollama pull llama3.1:70b

Step 3: Test It

# Interactive chat
ollama run llama3.1:8b

# API call (compatible with OpenAI format)
curl http://localhost:11434/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "llama3.1:8b",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

Step 4: Connect to OpenClaw

Ollama exposes an OpenAI-compatible API at http://localhost:11434. You can route OpenClaw tasks to it by configuring your agent to use the local endpoint for specific task types. Alternatively, use OpenRouter as a proxy to dynamically route between local and cloud models.

Performance Tuning Tips

• Close other apps — every GB of memory freed up is available for larger models or more KV cache
• Use Q4_K_M quantization — best balance of quality and speed for most models
• Set OLLAMA_NUM_PARALLEL=1 if you want maximum single-request speed
• Consider MLX format via LM Studio — 30–40% faster than GGUF on Apple Silicon
• Keep models loaded — set OLLAMA_KEEP_ALIVE=-1 to avoid reload latency

10. Our Recommendations

References