Decentralized GPU Compute Platform
Budget: $250 – $750 USD
Our project is moving into it's fourth major iteration and I’m ready to turn our vision of a decentralized NVIDIA H100 “super-cluster” into production reality. The system will let independent operators spin up GPU nodes, pool them into a single compute mesh, and have workloads automatically routed to whichever marketplace is paying the best rate at that moment.
My current stack direction is Python for the core services and Kubernetes to orchestrate the GPU containers across diverse hosts. You’ll be shaping a high-performance, fault-tolerant backend that can scale from dozens to thousands of nodes without manual babysitting.
Phase 1 focuses on three cornerstone capabilities:
• Automated workload distribution – smart scheduling that assigns jobs to the right GPU in milliseconds.
• Node monitoring and management – real-time health, performance metrics, and self-healing logic.
• Payment integration – accurate metering plus on-chain settlement so operators are paid automatically for every compute cycle they contribute.
Core features I need implemented:
• Scalable GPU clusters: nodes should auto-register, benchmark themselves, and be orchestrated (Kubernetes or a comparable container scheduler is fine) so that training jobs can scale out transparently.
• User-friendly interfaces: a simple web dashboard plus a CLI/SDK that lets data scientists submit, monitor, and cancel jobs without wrestling with low-level configs.
• Real-time monitoring: live GPU utilisation, temperature, job progress, and cost metrics streamed via Prometheus/Grafana or an equivalent stack.
Subsequent phases will expand the API surface, strengthen security, and refine marketplace integrations; I’m aiming for an ongoing collaboration, not a one-off sprint.
If you’ve built distributed systems, high-throughput micro-services, or any infrastructure that juggles GPUs at scale, I’d love to see it. Links, repos, or short case studies are all welcome.
The budget is flexible and will track closely with proven expertise. Let’s discuss milestones, agree on clear acceptance tests, and start connecting those H100s.
My current stack direction is Python for the core services and Kubernetes to orchestrate the GPU containers across diverse hosts. You’ll be shaping a high-performance, fault-tolerant backend that can scale from dozens to thousands of nodes without manual babysitting.
Phase 1 focuses on three cornerstone capabilities:
• Automated workload distribution – smart scheduling that assigns jobs to the right GPU in milliseconds.
• Node monitoring and management – real-time health, performance metrics, and self-healing logic.
• Payment integration – accurate metering plus on-chain settlement so operators are paid automatically for every compute cycle they contribute.
Core features I need implemented:
• Scalable GPU clusters: nodes should auto-register, benchmark themselves, and be orchestrated (Kubernetes or a comparable container scheduler is fine) so that training jobs can scale out transparently.
• User-friendly interfaces: a simple web dashboard plus a CLI/SDK that lets data scientists submit, monitor, and cancel jobs without wrestling with low-level configs.
• Real-time monitoring: live GPU utilisation, temperature, job progress, and cost metrics streamed via Prometheus/Grafana or an equivalent stack.
Subsequent phases will expand the API surface, strengthen security, and refine marketplace integrations; I’m aiming for an ongoing collaboration, not a one-off sprint.
If you’ve built distributed systems, high-throughput micro-services, or any infrastructure that juggles GPUs at scale, I’d love to see it. Links, repos, or short case studies are all welcome.
The budget is flexible and will track closely with proven expertise. Let’s discuss milestones, agree on clear acceptance tests, and start connecting those H100s.