🚀 VRAM-Relay Node

VRAM-Relay is a high-performance infrastructure designed to virtualize and offload GPU memory (VRAM) from mobile devices to remote compute servers.

Built for the era of local-first AI, it enables Android devices to run heavyweight models (LLMs, Stable Diffusion, vision models) by transparently leveraging the raw GPU power of a nearby PC or server.

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🏗️ System Architecture

1. Relay Server (Linux / CUDA)

The server node is responsible for GPU memory management and AI execution.

• VRAM Manager Dynamically allocates and releases GPU buffers based on client sessions and model requirements.

• Socket Engine An asynchronous, high-throughput TCP server capable of handling multiple concurrent inference streams.

• Discovery Provider A lightweight UDP broadcast service allowing instant detection of relay nodes by mobile devices on the local network.

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2. Android Client (JNI / C++ / Java)

The Android client is optimized for minimal latency and seamless integration.

• Native Bridge Ultra-fast C/C++ layer (JNI) to reduce serialization and network overhead.

• Discovery Service A background Android Service that scans the local Wi-Fi network for available relay nodes.

• Transparent API A simplified Java/Kotlin interface for loading models, sending prompts, and retrieving tensors or text outputs.

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3. "VRAM-Link" Protocol

• Pure Binary Protocol No JSON, no HTTP — minimal overhead with deterministic performance.

• Fixed 16-byte Header Includes packet type, payload size, sequencing, and CRC checksum.

• Network Optimization Optional LZ4 compression for large tensor transfers and model weights.

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🛠️ Installation & Deployment

Server Requirements

• OS: Linux (Ubuntu 22.04+ recommended)
• Hardware: NVIDIA GPU (Pascal architecture or newer)
• Software: Docker & NVIDIA Container Toolkit

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1. Docker Deployment

# Clone the repository
git clone https://github.com/your-repo/vram-relay.git
cd vram-relay

# Build the optimized Docker image
sudo docker build -t vram-relay-node -f docker/Dockerfile .

# Run the relay node
# Ports:
#  - 8765 TCP: data & inference
#  - 8766 UDP: service discovery
sudo docker run --gpus all \
  -p 8765:8765/tcp \
  -p 8766:8766/udp \
  --restart unless-stopped \
  --name vram-relay \
  vram-relay-node

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2. Android Client Setup

1. Open the android-client/ directory in Android Studio.
2. Ensure network permissions are present in AndroidManifest.xml (already included).
3. Build the project:

./gradlew assembleDebug

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📊 Performance & Latency (Wi-Fi 6 / LAN)

The protocol is optimized for near real-time responsiveness.

Operation	Latency (ms)	Effective Throughput
UDP Discovery	< 100 ms	N/A
RTT (Ping / Pong)	2 – 8 ms	< 1 KB
Model Loading	200 – 1200 ms	Up to 120 MB/s
LLM Inference	50 – 250 ms	GPU-dependent

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💻 Integration Example (Java)

VRAMRelayClient client = new VRAMRelayClient(context);
client.initialize();

// 1. Automatic server discovery
client.discoverServers(3000, servers -> {
    if (!servers.isEmpty()) {
        // 2. Connect to the first available GPU node
        client.connect(servers.get(0));

        // 3. Load a model into remote VRAM
        client.loadModel("llama-3-8b-q4", true);
    }
});

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🛡️ Security & Best Practices

⚠️ Important

• Local Network Only The protocol is unencrypted by default to maximize throughput. For Internet access, always use a secure VPN tunnel (WireGuard or Tailscale).

• Process Isolation Docker confines the relay process and limits access to the host file system.

• Timeout Handling Idle connections automatically release allocated VRAM after 5 minutes of inactivity.

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🗺️ Roadmap

• Multi-GPU support (NVLink / SLI)
• Token-by-token streaming inference
• Adaptive compression based on Wi-Fi signal quality
• Native plugins for PyTorch and Hugging Face

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📄 License & Contributions

This project is distributed under the MIT License.

Contributions are welcome via Pull Requests. Bug reports, performance improvements, and protocol extensions are highly encouraged.

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VRAM-Relay aims to break the VRAM barrier and bring truly powerful AI inference to mobile and constrained devices — without compromise.