Decentralized AI Compute Network — GPU Mining & Blockchain Rewards
Budget / SalaryHourly project
TypeFreelance project
LocationRemote
Posted1 hour ago
A decentralized AI compute platform designed to connect GPU providers with AI workloads through a distributed computing network. Instead of running AI workloads entirely on centralized cloud infrastructure, the platform allows independent miners to contribute GPU resources for tasks such as AI inference, model processing, fine-tuning, and other compute-intensive workloads.
The system coordinates available GPU resources, assigns workloads, monitors execution, and records completed compute jobs. A blockchain-based reward mechanism is used to track contributions and provide miners with transparent incentives based on the compute they provide.
The architecture separates the blockchain layer from the actual AI computation: blockchain infrastructure handles identity, job accounting, verification, and rewards, while GPU workers perform the computational workloads. This makes the system more scalable and allows additional AI capabilities and GPU providers to be introduced without redesigning the core network.
Key Features
- Decentralized GPU provider network
- AI workload distribution across available GPU workers
- Automated job scheduling and worker allocation
- GPU resource and worker health monitoring
- Real-time job execution and status tracking
- Blockchain-based compute contribution accounting
- Transparent miner reward mechanism
- Support for AI inference and compute-intensive workloads
- Dockerized GPU worker environments
- Secure communication between workers and orchestration services
- Web dashboard for monitoring compute activity
- Scalable architecture for adding additional GPU providers
- Fault handling and recovery for unavailable workers
- Compute usage and reward history
- Network statistics and performance monitoring
Responsibilities
- Designed the architecture for the decentralized AI compute network.
- Developed backend services for GPU worker registration, workload management, and job orchestration.
- Implemented the communication layer between the central coordination services and distributed GPU workers.
- Built AI execution services capable of running workloads on CUDA-enabled GPUs.
- Designed the compute accounting and miner reward workflow.
- Integrated blockchain functionality for transparent contribution and reward tracking.
- Developed APIs for managing users, workers, jobs, compute resources, and rewards.
- Implemented real-time monitoring of GPU workers and workload execution.
- Containerized AI workers and supporting services using Docker.
- Designed database structures for tracking jobs, compute usage, workers, and reward history.
- Implemented fault-tolerance mechanisms for failed or disconnected GPU workers.
- Built the web dashboard for monitoring network activity and compute performance.
- Optimized workload distribution and GPU utilization for scalable distributed execution.
- Set up development and deployment workflows using Git and CI/CD practices.
The system coordinates available GPU resources, assigns workloads, monitors execution, and records completed compute jobs. A blockchain-based reward mechanism is used to track contributions and provide miners with transparent incentives based on the compute they provide.
The architecture separates the blockchain layer from the actual AI computation: blockchain infrastructure handles identity, job accounting, verification, and rewards, while GPU workers perform the computational workloads. This makes the system more scalable and allows additional AI capabilities and GPU providers to be introduced without redesigning the core network.
Key Features
- Decentralized GPU provider network
- AI workload distribution across available GPU workers
- Automated job scheduling and worker allocation
- GPU resource and worker health monitoring
- Real-time job execution and status tracking
- Blockchain-based compute contribution accounting
- Transparent miner reward mechanism
- Support for AI inference and compute-intensive workloads
- Dockerized GPU worker environments
- Secure communication between workers and orchestration services
- Web dashboard for monitoring compute activity
- Scalable architecture for adding additional GPU providers
- Fault handling and recovery for unavailable workers
- Compute usage and reward history
- Network statistics and performance monitoring
Responsibilities
- Designed the architecture for the decentralized AI compute network.
- Developed backend services for GPU worker registration, workload management, and job orchestration.
- Implemented the communication layer between the central coordination services and distributed GPU workers.
- Built AI execution services capable of running workloads on CUDA-enabled GPUs.
- Designed the compute accounting and miner reward workflow.
- Integrated blockchain functionality for transparent contribution and reward tracking.
- Developed APIs for managing users, workers, jobs, compute resources, and rewards.
- Implemented real-time monitoring of GPU workers and workload execution.
- Containerized AI workers and supporting services using Docker.
- Designed database structures for tracking jobs, compute usage, workers, and reward history.
- Implemented fault-tolerance mechanisms for failed or disconnected GPU workers.
- Built the web dashboard for monitoring network activity and compute performance.
- Optimized workload distribution and GPU utilization for scalable distributed execution.
- Set up development and deployment workflows using Git and CI/CD practices.
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