⚡ macOS MLX Control Center v0.4 (Vision & Multimodal Edition)

The simplest, fastest 1-click Web GUI & CLI Control Center to search, download, compare, benchmark, and run local Multimodal Vision & Text LLMs on Apple Silicon M-Series processors.

Designed specifically for macOS, macOS MLX Control Center bridges Apple's high-performance mlx-lm (text) and mlx-vlm (vision) frameworks with a dark glassmorphic web dashboard and terminal interface. It turns any Mac powered by Apple Silicon M-Series processors into a local AI power-station with zero configuration hassle.

Dashboard Screenshot

Search Models Compare Models Customize API for Local Coders like OpenCode or Pi Code

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⚡ 1-Click Launch Options (No Tech Experience Needed!)

Option A: 1-Line Terminal Instant Launcher (Recommended)

Open Terminal and paste this single command:

curl -sSL https://raw.githubusercontent.com/mypbs/mac-mlx-control-center/main/install.sh | bash

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Option B: Double-Click Launcher

1. Download or clone this repository to your Mac.
2. Double-click start.command in Finder.
3. The server launches and automatically opens the dashboard in your default browser (http://127.0.0.1:9998)!

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🌟 Why macOS MLX Control Center?

Most local LLM runners are either overly complex, hog system menubars, or require manual hand-editing of configuration JSON files for coding tools. macOS MLX Control Center solves this with:

• 👁️ Vision & Multimodal Image Recognition (v0.4): Full native integration with mlx-vlm! Run Gemma 4, Qwen 2.5/3.8 VL, PaliGemma, Pixtral, LLaVA, SmolVLM, and more with full image analysis, OCR, visual question answering, and chart understanding.
• 🖼️ Interactive Vision Test Chat (v0.4): Upload and preview images directly in the GUI test bench to test OpenAI-compatible multimodal payloads.
• ⚙️ Dual-Engine Auto-Detection (mlx-vlm + mlx-lm) (v0.4): Automatically detects model architectures and chooses the optimal inference engine without manual flags.
• 🔌 LibreChat & Coding Agent Multi-Modal Sync (v0.4): Direct compatibility with LibreChat (host.docker.internal:9999/v1), Pi Code, and OpenCode for image recognition and coding workflows.
• 🧠 macOS Memory & Process Manager (v0.3): Real-time visual breakdown of Apple Silicon Unified Memory (App, Wired, Compressed, Available Cache) with an interactive top process scanner (🔍 Manage RAM) and 1-click process termination to free up memory for larger models.
• 🔤 Model Sorting & Date Added (v0.3): Sort local and downloaded models by Name (A-Z / Z-A), Size (Largest / Smallest), and Date Added (Newest / Oldest) with exact timestamps on all model cards.
• 📱 Horizontal List Mode vs. Card Mode (v0.3): Toggle seamlessly between sleek horizontal list rows and spacious square card grid views across all model and search views.
• 🟢 Activity Monitor-Aligned RAM Accounting (v0.3): Accurate unified memory metrics matching Activity Monitor, htop, and mactop.
• 🚀 1-Click Model Launching & Hot Swapping: Instantly switch between MLX models without terminal commands.
• 📦 Multi-Quant & Subfolder HF Support: Direct support for repositories with multiple quantizations (e.g. orcarouter/Qwen3.8-27B-Uncensored-MLX/tree/main/4-bit). Pick exact bit precision (2-bit, 4-bit, 6-bit, 8-bit) and download only the weights you need instead of 80+ GB!
• 🌐 Supercharged Hugging Face Search: Search thousands of model weights directly with instant filter chips (⚡ MLX Models, 👁️ Vision & Multimodal, 🎯 4-Bit Quantized, 💻 Code Models) and sorting (Most Downloads, Most Liked, Recently Updated).
• 🤖 Built-In AI Model Comparison Engine: Select 2 to 4 models to generate an instant technical breakdown comparing quantization precision (2bit-DQ, OptiQ-4bit), RAM compatibility, and specialization — works 100% locally out-of-the-box with zero API keys or accounts required!
• 🟢 "Can I Run It?" Apple Silicon RAM Calculator: Live color-coded RAM budget badges (🟢 Fits Smoothly, 🟡 High RAM Pressure, 🔴 Swap Danger) computed against your Mac's actual available memory.
• 🤖 Auto-Sync Pi Code & OpenCode Agents: Automatically configures Pi Code and OpenCode under provider MyMac (http://127.0.0.1:9999/v1). Whenever you swap models in the GUI, your coding agents automatically route to the newly loaded model!
• 📊 Tokens/Second Speed Benchmark: Measure generation speed (tok/s), execution time, token counts, and GPU memory usage on Apple Silicon.
• ⚙️ Custom API Settings: Change default ports (9999, 8888, 8080), host binding (127.0.0.1 or 0.0.0.0 for LAN access across home Wi-Fi), and max output token budgets with built-in memory guidelines.

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🛠️ Prerequisites

Running local AI on your Mac has never been easier. You only need:

1. Any Mac with Apple Silicon M-Series Processors (with Unified Memory).
2. macOS (12.0 or newer).
3. Python 3.10+ (pre-installed on macOS or available via Homebrew/Xcode).

Note: Dependencies like mlx-lm and hf CLI are automatically installed on-demand via uvx. You do not need to pre-install heavy ML environments! Zero API keys or account sign-ups are required.

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🖥️ Terminal CLI Mode

Prefer working in the terminal? You can also use mlx.sh directly:

./mlx.sh

Key CLI Commands:

• ./mlx.sh list: View all downloaded local models and RAM sizes.
• ./mlx.sh search <query>: Search Hugging Face repositories directly from terminal.
• ./mlx.sh start: Interactive launcher with Fast Swap and Concurrent port options.
• ./mlx.sh stop: Emergency kill-switch for running servers.

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💡 How Token Limits & RAM Work on Mac

In the ⚙️ API Settings panel, you can configure your Max Output Tokens:

• Max Output Tokens: Specifies the maximum number of generated response + reasoning tokens the server produces. It does not limit your input prompt size.
• RAM Guidelines for Apple Silicon:
  • 2B – 8B Models (Gemma 4 4B, Qwen 7B): Set to 4096 – 8192 (Light KV Cache RAM impact ~0.5 GB).
  • 14B – 35B Models (Qwen 32B, DeepSeek 35B): Set to 4096 (Moderate RAM impact ~1.5–3 GB).
  • 70B+ Models: Set to 2048 – 4096 to prevent memory swapping.

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💬 Integrating with LibreChat (Local ChatGPT Web UI with Vision)

The combo of macOS MLX Control Center and LibreChat gives you a completely private, self-hosted ChatGPT-like experience with full multimodal vision support on your Mac.

🌟 Key Benefits:

• 🔒 100% Local & Private: No data leaves your Mac; runs entirely on Apple Silicon GPU & Unified Memory.
• 👁️ Full Multimodal Vision: Attach screenshots, PDFs, documents, diagrams, and photos in LibreChat for instant analysis with models like Gemma 4 and Qwen 2.5/3.8 VL.
• ⚡ Hot-Swapping: Switch models in MLX Control Center with 1 click; LibreChat dynamically routes to the active model via default_model without restarting containers.
• 💰 Zero Cost: No OpenAI/Anthropic API subscriptions needed.

🛠️ Setup Guide:

1. Configure librechat.yaml

Add the custom MLX endpoint to your librechat.yaml file:

version: 1.2.0

endpoints:
  custom:
    - name: "MacBook MLX"
      apiKey: "local"
      baseURL: "http://host.docker.internal:9999/v1" # Use http://127.0.0.1:9999/v1 if running LibreChat natively without Docker
      models:
        default:
          - "default_model"
          - "mlx-community/gemma-4-e4b-it-OptiQ-4bit"
          - "lmstudio-community/Qwen3.8-27B-MLX-4bit"
          - "mlx-community/Llama-3.2-3B-Instruct-4bit"
        fetch: true
      titleConvo: true
      modelDisplayLabel: "MacBook MLX"

Note for Docker users: host.docker.internal allows Docker containers on macOS to seamlessly communicate with the MLX server running directly on macOS.

2. Restart LibreChat

If running via Docker:

docker compose restart

3. Chat & Analyze Images

1. Open LibreChat (e.g. http://localhost:3080 or http://localhost:3081).
2. Select MacBook MLX from the model dropdown.
3. Drag & drop images directly into the chat to perform OCR, code transcription, and visual reasoning!

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🤖 Agentic AI Pairing Instructions (Antigravity, ChatGPT, Claude, Cursor & Codex)

You can use an AI coding assistant (like Google Antigravity, ChatGPT / Codex, Claude Code, or Cursor) to completely automate managing, launching, and integrating this system on your Mac.

Here are ready-to-use prompt templates you can copy and paste into your AI assistant:

📋 Prompt 1: 1-Click Setup & Launcher

"I want to run local AI models on my Apple Silicon Mac using macOS MLX Control Center. Please run curl -sSL https://raw.githubusercontent.com/mypbs/mac-mlx-control-center/main/install.sh | bash to set up the repository, start the GUI dashboard on port 9998, and verify that the server is responding."

📋 Prompt 2: Auto-Configure LibreChat with MLX Vision

"Please configure my local LibreChat setup to connect to my MLX Control Center. Update librechat.yaml with a custom endpoint named 'MacBook MLX' pointing to http://host.docker.internal:9999/v1 with fetch: true and models list containing default_model and mlx-community/gemma-4-e4b-it-OptiQ-4bit. Then restart the LibreChat docker container and confirm health."

📋 Prompt 3: Auto-Configure Coding Agents (Pi Code / OpenCode / Cline)

"Please configure my local coding agent (Pi Code / OpenCode) to use my local MLX server as the default provider under the name 'MyMac' at http://127.0.0.1:9999/v1 with model ID default_model and reasoning tokens enabled."

📋 Prompt 4: Diagnostic & Vision Troubleshooting

"Check the status of my MLX server on port 9999 and GUI on port 9998 using curl http://127.0.0.1:9998/api/status. If I'm using a vision model (like Gemma 4), verify that it is running via mlx-vlm so image uploads work in LibreChat."

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🤝 Agent Integration (Pi Code & OpenCode)

When auto-sync is enabled in ⚙️ API Settings, launching any model automatically updates your local coding agent configs:

• Pi Code: ~/.pi/agent/settings.json & ~/.pi/agent/models.json
• OpenCode: ~/.config/opencode/opencode.jsonc

Provider Name: MyMac
Model ID: default_model (Auto-Detect Active MLX Endpoint)

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🤖 Acknowledgements

• Google Antigravity AI: Pair-programmed, engineered, and designed with Antigravity AI.
• Apple MLX Team: Powered by Apple's open-source mlx-lm & mlx-vlm frameworks.

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📜 License

MIT License — free to use, modify, and share!