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Artificial Intelligence

Hednet is building a new decentralized infrastructure layer for Artificial Intelligence—one that harnesses idle CPU and GPU resources from users worldwide to power AI training, inference, and inter-agent collaboration at scale. By shifting the foundation of AI development away from centralized cloud servers, Hednet enables a more affordable, accessible, and autonomous AI future.

AI Agents​

Hednet supports the deployment and operation of autonomous AI agents—intelligent systems capable of sensing their environment, making decisions, and taking actions toward defined goals. Through Hednet's decentralized compute layer, these agents are no longer confined to centralized data centers. They can operate at the edge, scale dynamically across nodes, and respond in real-time.

AI agents on Hednet benefit from:​

  • Peer-to-peer communication with other agents.

  • Real-time access to distributed compute power.

  • Adaptive learning in open, decentralized environments.

Decentralized AI Training (Machine Learning & Deep Learning)​

Training modern AI models requires vast GPU power and compute time—resources traditionally monopolized by expensive cloud providers. Hednet unlocks this capability by aggregating idle CPUs and GPUs from its global network.

The platform supports a full range of AI workloads, including:​

  • Natural Language Processing (NLP): Text classification, chatbot training, language modeling.

  • Large Language Models (LLMs): Fine-tuning transformer-based architectures like GPT.

  • Computer Vision: Object detection, image segmentation, and video analysis.

  • Multimodal Models (LMMs): Unified understanding of text, image, and audio inputs.

With Hednet, anyone—from independent developers to research teams—can train, test, and deploy machine learning and deep learning models at a fraction of traditional cloud costs.

Decentralized AI-to-AI​

Hednet is not just about running models; it’s about enabling AI agents to learn from and collaborate with each other—securely and autonomously.

The network supports decentralized AI-to-AI systems through:

  • Graph Neural Networks (GNNs): Mapping relationships and enabling information sharing between agents.

  • Hypergraph Learning: Powering complex, multi-agent coordination.

  • Zero-Knowledge Proofs (ZKPs): Ensuring agents can validate outcomes or share knowledge without exposing private data.

This allows Hednet to power intelligent ecosystems where AI agents can:​

  • Self-organize into collaborative networks.

  • Co-train models across distributed nodes.

  • Execute collective decision-making workflows—without any centralized control.

By tapping into global compute and enabling AI agents to train, operate, and collaborate independently, Hednet transforms Artificial Intelligence into a truly decentralized capability—built for scale, privacy, and long-term evolution.