The problem GPU-IP solves

Cost-Efficient Computing

• Pay-as-you-go model reduces upfront costs.
• Access high-performance GPUs without ownership commitments.

Innovation Accelerator:

• Empowers developers and researchers with on-demand GPU resources.
• Accelerates tasks like AI, machine learning, and rendering.

Decentralized Security:

• Utilizes blockchain for transparent and secure transactions.
• Minimizes risks associated with central points of failure.

Collaborative Efficiency:

• Allows concurrent GPU usage for collaborative projects.
• Fosters teamwork and innovation within communities.

Sustainable Computing:

• Reduces electronic waste by optimizing GPU utilization.
• Promotes eco-friendly practices in computational resource management.

Challenges we ran into

Concurrency and Synchronization:

Challenge: Ensuring that multiple users can concurrently allocate and deallocate GPUs without conflicts.
Solution: Implement robust locking mechanisms or explore state channels to manage concurrent transactions efficiently.

Problems with JuiceLabs:

Challenge: Juice was not cooperating and posed many problems
Solution: We were persistent and finally solved all the issues.