OpenGPU Mesh – Decentralized GPU Compute Sharing Network

High-performance GPUs are expensive and inaccessible for many students and open-source contributors. Meanwhile, thousands of GPUs remain idle on personal systems during off-hours.

This leads to:

• Limited AI/ML research access

• Slower innovation in open-source

• Inefficient hardware utilization

💡 Solution

OpenGPU Mesh is a decentralized compute-sharing network that enables:

• Users to share idle GPU resources

• Students to request temporary compute access

• Token-based fair usage tracking

• Secure, containerized execution of jobs

It transforms unused GPUs into a distributed open compute grid.

🏗 How It Works

1️⃣ Node Registration

Users install a lightweight GPU agent:

• Detects available GPU (via NVIDIA-SMI)

• Registers node with central scheduler

• Reports idle capacity

2️⃣ Job Submission

Students:

• Submit ML training job (Docker container)

• Specify required GPU memory & time

• Receive token cost estimation

3️⃣ Scheduler Allocation

Backend:

• Matches job to available GPU node

• Deploys container securely

• Monitors execution via WebSockets

4️⃣ Token System

• Contributors earn tokens by sharing GPU

• Users spend tokens when running jobs

• Ensures fair and sustainable ecosystem

🛠 Tech Stack

Backend

• FastAPI (API server)

• Python scheduler engine

• WebSockets (real-time monitoring)

• Docker (secure job isolation)

Orchestration

• Kubernetes (future scalability)

• Distributed node registry

GPU Management

• NVIDIA-SMI monitoring

• CUDA compatibility check

Security

• Container sandboxing

• Resource limitation

• Time-based job kill switch