JARVIS OS v17.0

The Voice-First Distributed AI Operating System

A 9-layer autonomous operating system spanning boot to voice, running 928+ AI agents across a multi-GPU cluster without any cloud dependency.

🌟 Overview

JARVIS OS is a hyper-localized, distributed orchestration engine designed to manage an entire home infrastructure, multi-agent reasoning systems, and autonomous trading bots entirely on-premise. It is built to run on multi-GPU Linux clusters, ensuring maximum privacy, zero cloud latency, and absolute sovereignty over data and AI execution.

Key Metrics

🏗️ System Architecture (La Creatrice)

JARVIS OS operates on a distributed architecture, distributing cognitive load across specialized hardware nodes.

graph TD;
    subgraph "JARVIS Cluster (Zero-Cloud)"
    M1[M1: Primary Orchestrator<br>Ryzen 5700X3D<br>Voice & Main Logic] <-->|Sync| DB[(Distributed SQLite3)]
    M2[M2: Deep Reasoning<br>3 GPUs - 24GB VRAM<br>DeepSeek-R1] <-->|Consensus| M1
    M3[M3: Fallback Node<br>Redundancy & Backup] -.->|Failover| M1
    OL1[OL1: Edge Worker<br>Lightweight Local Tasks] <-->|Task Exec| M1
    end
    
    User((User)) -->|Voice <300ms| M1
    M1 -->|Trading Signals| MEXC[MEXC Exchange]
    M1 -->|Automation| N8N[n8n Workflows]

The 9-Layer Cognitive Stack

1. Hardware Layer: Multi-machine GPU cluster (CUDA, ROCm).
2. OS Layer: Ubuntu/Debian optimized with custom kernel parameters.
3. Data Layer: Distributed synchronized SQLite3 (BASE-SQL3-COMMUNE).
4. Inference Layer: LM Studio, Ollama, vLLM load balancing.
5. Protocol Layer: Model Context Protocol (MCP) handlers.
6. Agent Layer: Specialized agents (Trading, Coding, OS Management).
7. Orchestration Layer: Claude Agent SDK / JARVIS Core.
8. Pipeline Layer: Domino Chains (n8n, Python execution).
9. Interface Layer: Real-time CUDA Whisper Voice Interface & WebSocket Dashboards.

🚀 Getting Started

Prerequisites

• OS: Linux (Ubuntu 22.04+ recommended)
• Hardware: Minimum 1x NVIDIA GPU with 8GB VRAM (6+ GPUs across cluster recommended for full swarm mode).
• Dependencies: Python 3.11+, CUDA Toolkit 11.8+, Docker.

Installation

1. Clone the OS repository:

   git clone https://github.com/Turbo31150/jarvis-linux.git
   cd jarvis-linux
   

2. Initialize the Cluster Environment:
   Run the main initialization script to setup the Python virtual environment and install MCP core tools.

   ./scripts/init_cluster.sh
   

3. Configure Database Synchronization:
   Ensure BASE-SQL3-COMMUNE is cloned in your shared storage directory and point the .env configuration to it.

   cp .env.example .env
   # Edit .env with your absolute paths and API keys (if relying on fallback providers)
   

4. Start the Orchestrator:

   systemctl --user start jarvis-orchestrator
   

🎙️ Voice Subsystem (Whisper Flow)

JARVIS OS is primarily designed to be interacted with via voice. The voice pipeline relies on a highly optimized custom implementation of Whisper running on CUDA, ensuring a sub-300ms response time from spoken word to command execution.

To test the voice pipeline standalone:

python3 src/voice/whisper_listener.py --mic-index 1 --model large-v3 --cuda-optimize

🛡️ Security & Privacy

JARVIS OS is built on the philosophy of the Saaspocalypse — rejecting cloud-dependent, opaque SaaS platforms.

• Air-Gapped Capable: All critical reasoning runs locally. External internet is only required for execution targets (like MEXC trading or web scraping).
• Data Sovereignty: No telemetry, no cloud syncing of voice data, local SQLite database.

👨‍💻 Contributing

Given the deeply personal and hyper-customized nature of this OS to my specific home hardware cluster, direct Pull Requests may require significant adaptation. However, forks, discussions, and architectural debates are highly welcome.

📄 License

MIT License. See LICENSE for details.