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Decentralized AI Inference

GPU

The Future of Compute​

Traditional AI infrastructures are centralized, costly, and vulnerable to control and censorship. SKAINET introduces a paradigm shift by leveraging a decentralized network of devices to perform AI inference and training.

Key Features​

  • Global Network of Devices: Harnessing the computational power of consumer hardware worldwide.
  • Sharded Model Processing: Distributing large AI models across multiple devices for efficient inference.
  • Latency Optimization: Batching computations to minimize delays and maximize throughput.

How It Works​

  1. Device Integration: Users download the SKAINET software client for their device's operating system (macOS, Windows, Linux, iOS, Android).
  2. Network Participation: Devices become nodes in the SKAINET network, contributing computational resources.
  3. Workload Distribution: AI models are segmented, and tasks are allocated to devices based on their capabilities.
  4. Incentivization: Users earn rewards proportional to their contribution (uptime, computational power).

Benefits​

  • Scalability: As more devices join, the network's computational capacity grows.
  • Cost-Efficiency: Utilizing existing hardware reduces the need for expensive infrastructure.
  • Resilience: Decentralization mitigates the risk of single points of failure or control.

Privacy and Security​

  • Data Protection: Computations are performed locally, reducing the need to transmit sensitive data.
  • Trustless Environment: Consensus mechanisms ensure integrity without relying on centralized authorities.
  • Secure Communications: Encryption and secure protocols safeguard interactions within the network.