The demand for AI computing power is growing rapidly, with projections suggesting that the global AI industry could exceed $500 billion by 2024. AI is revolutionizing sectors like healthcare and finance, with GPU computing playing a pivotal role. However, the high cost of acquiring and maintaining powerful GPUs presents a major hurdle, especially for smaller businesses.

Entities such as NeurochainAI, Nvidia, and AMD are working to democratize AI computation. NeurochainAI leverages distributed computing via their platform, while Nvidia and AMD provide GPUs. Distributed computing addresses the challenge by allowing businesses to utilize idle GPU capacities, accessing computational power as needed without hefty upfront expenses.

As the need for AI computation intensifies, so does the demand for robust GPU capabilities. GPUs excel in handling the intensive mathematical calculations required by AI, such as matrix operations and vector computations. Unlike CPUs, which process tasks sequentially and have fewer cores, GPUs are designed for parallel processing, making them ideal for accelerating AI model training and data processing tasks.

Despite the advantages of GPUs, their procurement and upkeep costs remain prohibitive for many SMEs. Additionally, rapid technological advancements mean GPUs can quickly become outdated, posing further investment risks.

Distributed computing offers a viable solution by tapping into underutilized GPU resources across desktops, gaming systems, and data centers. This approach not only optimizes GPU usage but also allows owners to monetize idle hardware by renting out computational power. Businesses benefit from scalable, cost-effective GPU access without the burden of ownership and maintenance costs.

The infrastructure supporting distributed computing plays a crucial role, ensuring secure, reliable, and efficient GPU resource allocation. Advanced network hardware, fast internet connectivity, and robust security measures like encryption and backup systems are essential components. Platforms like NeurochainAI exemplify effective distributed computing by facilitating seamless GPU supply-demand matching, enhancing performance and cost efficiency.

In conclusion, distributed computing is pivotal in meeting the escalating demand for AI computation by maximizing GPU resource utilization, lowering costs, and enhancing accessibility to advanced AI technologies.