Problem Statement
Artificial Intelligence is advancing faster than ever, but the infrastructure powering it remains broken. While model complexity and data scale have increased exponentially, the availability and accessibility of GPU compute has lagged far behind.
This mismatch has created two massive barriers to innovation: an imbalance between GPU supply and demand, and unsustainable infrastructure costs.
1. Supply-Demand Imbalance
Despite global growth in deployed GPUs, a significant portion of this hardware remains idle:
Over 40% of GPUs worldwide sit underutilized or idle
These include consumer-grade GPUs, academic clusters, enterprise workstations, and crypto rigs
Most are siloed, uncoordinated, and unavailable to the broader AI community
Meanwhile, demand for compute is exploding:
Training a large language model (LLM) or foundation model can require millions of GPU hours
Startups and researchers are being priced out, with limited access to enterprise-scale clusters
This has created a global supply bottleneck. GPU-rich corporations continue to dominate AI development, while innovators without infrastructure are forced to wait, pay inflated prices, or give up altogether.
2. High Cost of AI Infrastructure
Building your own AI cluster is not only expensive—it’s operationally intensive.
Setting up a scalable GPU cluster with high-bandwidth networking, redundancy, and storage can cost $300,000–$500,000+
Maintenance includes DevOps, cooling, uptime guarantees, and hardware replacement
Teams also need to manage security, data privacy, compliance, and parallel workload orchestration
Most small-to-medium enterprises (SMEs) and independent researchers simply can’t afford this. Even cloud platforms are:
Charging $2.50–$3.00 per GPU hour
Enforcing usage caps and long provisioning delays
Offering limited transparency on performance or availability
The result: a two-tiered AI economy. One with compute — and one without.
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