👁️🗨️Phase 4: Autonomous LLM Infrastructure & Expansion
Launch of BrainHub (LLM Execution Agent) BrainHub goes live, enabling users to train and deploy large language models (LLMs) and other AI workloads across QuantZ’s decentralized compute mesh. This agent abstracts complex infrastructure needs, allowing seamless access to distributed AI capabilities without centralized gatekeepers.
AI-Native Resource Allocation (MCP + Automation Layer) Full integration of AI-powered task automation within the MCP runtime—allowing predictive scaling, intelligent resource matching, and dynamic load balancing across compute nodes to optimize high-intensity workloads like AI training.
End-to-End Security & Performance Reinforcement Implementation of zero-trust execution layers, decentralized encryption key management, and compute validation protocols to ensure verifiability and security of sensitive AI operations on distributed nodes.
Edge Compute & Agent Mobility Enhancements Enable support for edge-device participation in AI processing tasks, improving latency and resilience by decentralizing inference and micro-tasks to the device layer via lightweight MCP agents.
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