# Problem Statement

The escalating demand for GPU computing power, driven by the increasing complexity and volume of AI/ML workloads, presents significant challenges for organizations. Major cloud providers impose complex permission models, long-term contracts, and high costs, making it difficult to quickly scale capacity to meet demand. This gap is expected to widen as the need for GPU computing continues to grow, necessitating a 2-3-fold increase in cloud GPU capacity in the near future.

Meanwhile, a substantial portion of global GPU capacity remains underutilized:

* Independent Data Centers: The average server utilization rate in US data centers was around 18% in 2019, indicating a significant amount of idle capacity (Source: DatacenterDynamics, 2019).
* Crypto Miners: The Ethereum transition has left many crypto miners with idle GPUs, resulting in a surplus of mining hardware (Source: TechSpot, 2022).
* Consumer GPUs: Many consumers use their GPUs for low-intensity tasks, leaving a significant portion of GPU capacity underutilized (Source: Tom's Hardware, 2020).

To address this imbalance, there is a critical need for solutions that can efficiently leverage this underutilized GPU capacity, integrating it seamlessly into existing workflows and meeting the surging demand for compute power without the prohibitive costs and constraints imposed by major cloud providers.

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