Introduction

The Compute Wall

The advancement of artificial intelligence (AI) increasingly depends on access to high-performance GPUs. As models grow in size and complexity, developers face the “Compute Wall” a point where centralized infrastructure becomes too expensive, limited, or inefficient to scale further. This constraint slows AI innovation, creates bottlenecks, and restricts access to key players. Zenqira addresses this by introducing a DePIN-based solution (Decentralized Physical Infrastructure Network) that connects idle GPUs from individuals, labs, and data centers into a global compute layer. Contributors earn ZENQ tokens while AI builders gain cost-effective, private, and censorship-resistant access to compute. This creates a more inclusive and secure infrastructure to power the next generation of AI.

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Understanding the Compute Wall

The compute wall emerges when access to GPU power is no longer scalable through traditional channels. This affects every AI discipline:

• Language Models: Running or fine-tuning LLMs requires costly multi-GPU clusters.

• Vision and Multimodal Models: Training on large datasets overwhelms available compute.

• Reinforcement Learning and Simulation: High-frequency environments need persistent, distributed processing.

Contributing factors include:

• Centralized Supply and Control: Major cloud providers restrict access, apply censorship policies, and expose user data.

• High Cost of Compute: Usage-based cloud pricing inflates costs for extended training.

• Underused Private Hardware: Millions of GPUs in homes and institutions are idle and inaccessible.

• Privacy Concerns: Centralized clouds require full data exposure, introducing risks for sensitive workloads.

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Challenges in AI Compute Access

1. Limited Access to Scalable Infrastructure AI developers often face long wait times and capacity restrictions when accessing cloud GPUs. Large institutions dominate supply while small teams and independent researchers struggle to compete.

2. High Cost of Centralized Compute Cloud platforms like AWS, Azure, and Google Cloud charge premium rates for high-performance GPUs. Long-term usage becomes unsustainable for most startups and research labs, stalling innovation and experimentation.

3. Privacy and Data Control Risks Using centralized infrastructure requires uploading sensitive datasets to third-party platforms. This raises concerns around data privacy, model security, and intellectual property exposure.

4. Underutilized Global GPU Supply Millions of GPUs in homes, gaming rigs, research labs, and small data centers sit idle or underused. There is no mainstream system that connects this global resource pool with active AI compute demand.

5. Lack of Incentives for Contributors Individuals with capable hardware have no seamless way to monetize their GPU capacity. Without a reliable decentralized protocol for contributing and earning, these resources remain locked away from the AI economy.