AI Compute Nodes (GPUs)

AI Compute Nodes form the backbone of the OmniTensor decentralized AI compute network, enabling distributed and scalable AI inference and model training tasks. These nodes leverage GPU resources contributed by the community to provide computational power for AI-driven dApps, data processing, and machine learning models, offering a decentralized alternative to traditional cloud-based solutions.

Node Architecture and Role

AI Compute Nodes are designed to process high-complexity AI tasks by utilizing GPUs for parallel processing. The nodes participate in the OmniTensor ecosystem by executing tasks such as:

  • AI Model Inference

    Running real-time inferences from trained models, including Large Language Models (LLMs), image recognition, and speech processing.

  • AI Model Training

    Contributing to distributed training of new models or the fine-tuning of existing models by utilizing the compute capacity of connected GPUs.

Each AI Compute Node is part of the broader AI OmniChain, a Layer 2 solution built on top of the Layer 1 EVM Chain, designed to ensure full scalability and interoperability within the AI ecosystem.

Node Types

  1. Desktop GPUs

    High-performance consumer and professional-grade GPUs

  2. Mobile NPUs/GPUs

    Specialized AI processors in modern smartphones

  3. ASIC AI Accelerators

    Custom hardware designed for AI computations

Hardware Requirements

Desktop GPUs

  • Minimum: NVIDIA GTX 1060 (6GB VRAM) or AMD RX 580 (8GB VRAM)

  • Recommended: NVIDIA RTX 3080 (10GB VRAM) or AMD RX 6800 XT (16GB VRAM)

  • CPU: 4+ cores, 3.0+ GHz

  • RAM: 16GB DDR4

  • Storage: 256GB NVMe SSD

  • OS: Ubuntu 20.04 LTS, Windows 10/11, or macOS 11+

Mobile Devices

  • SoC: Snapdragon 865+ or Apple A14 Bionic and newer

  • RAM: 6GB+

  • Storage: 64GB+

  • OS: Android 10+ or iOS 14+

Software Stack

  • OmniTensor Node Client v2.3.1

  • CUDA Toolkit 11.4+ (for NVIDIA GPUs)

  • ROCm 4.5+ (for AMD GPUs)

  • Docker 20.10+

  • Python 3.8+

Node Setup

Desktop GPU Setup

  1. Install dependencies:

sudo apt update && sudo apt install -y cuda-toolkit-11-4 docker.io python3-pip

  1. Pull the OmniTensor Node image:

docker pull omnitensor/compute-node:v1.3.1

  1. Run the node:

docker run -d --gpus all --name omni-compute \
  -v /path/to/node/data:/data \
  -p 8080:8080 \
  omnitensor/compute-node:v1.3.1

Mobile Device Setup

  1. Download the OmniTensor Mobile App from the App Store or Google Play Store.

  2. Open the app and follow the on-screen instructions to create or import your wallet.

  3. Navigate to the "Node" section and tap "Start Mining" to begin contributing your device's computational power.

AI Task Execution

AI Compute Nodes perform various tasks, including:

  1. Inference: Executing pre-trained models for real-time predictions

  2. Training: Participating in federated learning for model updates

  3. Optimization: Fine-tuning models for specific use cases

Task Allocation Algorithm

OmniTensor employs a sophisticated task allocation algorithm that considers:

  • Node computational capacity

  • Historical performance

  • Network latency

  • Task complexity

Performance Metrics

Monitor your node's performance using the following key metrics:

  • FLOPS (Floating Point Operations per Second)

  • GPU Utilization

  • Energy Efficiency (FLOPS/Watt)

  • Task Completion Rate

  • Reward Accrual

Access these metrics through the OmniTensor Dashboard or via API:

import omnitensor-sdk

node = omnitensor-sdk.Node(node_id="your_node_id")
metrics = node.get_performance_metrics()
print(f"GPU Utilization: {metrics['gpu_utilization']}%")
print(f"Task Completion Rate: {metrics['task_completion_rate']}%")

Reward Mechanism

AI Compute Nodes earn OMNIT tokens based on their contributions:

  1. Base Reward: Proportional to computational power provided

  2. Task Completion Bonus: Additional rewards for successfully completed tasks

  3. Consistency Multiplier: Increased rewards for nodes with high uptime

Rewards are calculated using the following formula:

Reward = (Base * Power) + (Bonus * Tasks) * Consistency

Where:

  • Base: Base reward rate (OMNIT/hour)

  • Power: Normalized computational power

  • Bonus: Per-task completion bonus

  • Tasks: Number of successfully completed tasks

  • Consistency: Uptime-based multiplier (1.0 - 1.5)

Advanced Features

Dynamic Overclocking

OmniTensor's node client includes an AI-driven dynamic overclocking feature to optimize performance:

from omnitensor_sdk import GPUOptimizer

optimizer = GPUOptimizer(safety_level=0.8)
optimal_settings = optimizer.find_optimal_settings()
optimizer.apply_settings(optimal_settings)

Federated Learning Integration

Participate in decentralized model training:

from omnitensor_sdk import FederatedLearner

learner = FederatedLearner(model="llm_v1", round_duration=3600)
learner.start_training()

Troubleshooting

Common issues and solutions:

  1. Low Task Allocation

    • Check network connectivity

    • Ensure GPU drivers are up-to-date

  2. High Rejection Rate

    • Verify CUDA/ROCm compatibility

    • Monitor for hardware instability

  3. Reward Discrepancies

    • Confirm wallet address is correctly linked

    • Review performance metrics for inconsistencies

Future Developments

The OmniTensor team is actively working on:

  • Multi-GPU support for increased parallelism

  • Integration with specialized AI hardware (e.g., TPUs, NPUs)

  • Advanced task scheduling using Reinforcement Learning

  • Cross-chain compute resource sharing

Stay tuned to our GitHub repository and community channels for updates and to participate in shaping the future of decentralized AI computation.

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