Mac Studio AI — The Most Powerful Local AI Machine#

The Mac Studio is the best hardware for local AI. Mac Studio M4 Ultra with 256GB of unified memory runs 120B+ parameter models. Mac Studio M3 Ultra with 512GB loads frontier models that need 4-8 NVIDIA A100s elsewhere. The Mac Studio runs everything in one memory pool — no PCIe bottleneck.

One Mac Studio is a powerhouse. Multiple Mac Studios become a fleet.

Mac Studio configurations for AI#

Mac Studio ConfigChipMemoryGPU CoresMac Studio LLM Sweet SpotMac Studio M4 MaxM4 Max128GB4070B models on Mac StudioMac Studio M4 UltraM4 Ultra256GB80120B+ models on Mac StudioMac Studio M3 UltraM3 Ultra192-512GB76236B models on Mac StudioMac Studio M2 UltraM2 Ultra192GB7670B-120B on Mac Studio

Setup your Mac Studio#

bash

pip install ollama-herd    # install on your Mac Studio
herd                       # start Mac Studio as the router (port 11435)
herd-node                  # connect additional Mac Studios or other devices

Mac Studios discover each other automatically on your local network.

Add Mac Studio image generation#

bash

uv tool install mflux           # Flux models (~5s at 512px on Mac Studio M4 Ultra)
uv tool install diffusionkit    # Stable Diffusion 3/3.5 on Mac Studio

Use your Mac Studio for AI inference#

Mac Studio LLM inference — run the biggest models#

python

from openai import OpenAI

# Connect to Mac Studio running Ollama Herd
mac_studio = OpenAI(base_url="http://mac-studio:11435/v1", api_key="not-needed")

# 120B model — runs smoothly on Mac Studio M4 Ultra (256GB unified memory)
response = mac_studio.chat.completions.create(
    model="gpt-oss:120b",  # loaded entirely in Mac Studio unified memory
    messages=[{"role": "user", "content": "How does Mac Studio handle large AI models?"}],
    stream=True,
)
for chunk in response:
    print(chunk.choices[0].delta.content or "", end="")

Mac Studio image generation#

bash

# Flux via mflux — ~5s on Mac Studio M4 Ultra
curl -o mac_studio_art.png http://mac-studio:11435/api/generate-image \
  -H "Content-Type: application/json" \
  -d '{"model": "z-image-turbo", "prompt": "a Mac Studio on a minimalist desk with holographic AI display", "width": 1024, "height": 1024}'

# Stable Diffusion 3 on Mac Studio — ~9s
curl -o mac_studio_sd3.png http://mac-studio:11435/api/generate-image \
  -H "Content-Type: application/json" \
  -d '{"model": "sd3-medium", "prompt": "Mac Studio M4 Ultra rendering AI art", "width": 1024, "height": 1024, "steps": 20}'

Mac Studio speech-to-text#

bash

# Transcribe on Mac Studio via Qwen3-ASR
curl http://mac-studio:11435/api/transcribe \
  -F "file=@mac_studio_meeting.wav" \
  -F "model=qwen3-asr"

Mac Studio embeddings#

bash

# Generate embeddings on Mac Studio
curl http://mac-studio:11435/api/embed \
  -d '{"model": "nomic-embed-text", "input": "Mac Studio M4 Ultra unified memory AI inference"}'

Recommended models for Mac Studio#

Mac Studio ConfigModels for this Mac StudioMac Studio M4 Max (128GB)llama3.3:70b, qwen3:72b, deepseek-r1:70b, codestralMac Studio M4 Ultra (256GB)gpt-oss:120b, qwen3:110b, two 70B models simultaneouslyMac Studio M3 Ultra (512GB)deepseek-v3:236b (quantized), multiple 70B models at once

Ask the Mac Studio for recommendations: GET http://mac-studio:11435/dashboard/api/recommendations

Multiple Mac Studios as a fleet#

text

Mac Studio #1 (M4 Ultra, 256GB)  ─┐
Mac Studio #2 (M4 Max, 128GB)    ├──→  Mac Studio Router (:11435)  ←──  Your apps
Mac Mini (32GB)                   ─┘

The Mac Studio router scores each device on 7 signals. Big models route to the Mac Studio with the most memory.

Monitor your Mac Studio#

Mac Studio dashboard at http://mac-studio:11435/dashboard — models loaded on each Mac Studio, queue depths, thermal state, memory.

bash

# Mac Studio fleet status
curl -s http://mac-studio:11435/fleet/status | python3 -m json.tool

# Mac Studio health checks
curl -s http://mac-studio:11435/dashboard/api/health | python3 -m json.tool

Example Mac Studio fleet status response:

json

{
  "fleet": {"nodes_online": 2, "nodes_total": 2},
  "nodes": [
    {"node_id": "Mac-Studio-Ultra", "memory": {"total_gb": 256, "used_gb": 120}},
    {"node_id": "Mac-Studio-Max", "memory": {"total_gb": 128, "used_gb": 85}}
  ]
}

Full documentation#

• Agent Setup Guide
• Image Generation Guide
• API Reference

Contribute#

Ollama Herd is open source (MIT). Built by Mac Studio owners for Mac Studio owners:

• Star on GitHub — help other Mac Studio users find us
• Open an issue — share your Mac Studio AI setup
• PRs welcome — CLAUDE.md gives AI agents full context. 444 tests, async Python.

Guardrails#

• No automatic downloads — Mac Studio model pulls require explicit user confirmation.
• Model deletion requires explicit user confirmation.
• All Mac Studio requests stay local — no data leaves your network.
• Never delete or modify files in ~/.fleet-manager/.