Many resources have been pivotal in shaping global economies, whether by functioning as currency (precious stones, salt, copper) or possessing qualities useful in industry (silver, petroleum). While demand for spices stimulated global trade, gold triggered a great tidal wave of prosperity, migration and conflicts. Oil, for its part, was the key ingredient driving technological and industrial revolution.
All of which is to say that certain precious commodities have retained a special significance throughout human history. And as we navigate the Digital Age, new resources are coming to the fore. One of them, particularly in the context of Big Data and AI, is GPU compute, which describes the architecture developers use to build algorithms, render 3D graphics, and more.
With the ongoing global rush to integrate AI into every app and program, Graphics Processing Units have almost become the de-facto currency of technological change. And competition for their control is seriously heating up.
Fuel for Our Tech-Driven World
Commodities like gold, fuel, and oil have been the lifeblood of economies and empires, catalysts for human progress and engines of economic growth. Alas, their inherent appeal has also caused untold conflicts including on a global scale.
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When gold was no longer sufficient to finance war, countries abandoned the gold standard en masse to continue manufacturing ships, tanks, and bullets. We don’t need to linger on the resultant body count to appreciate that conflict resulting from resource control has been hugely damaging.
As critical as the aforementioned resources have been, today’s world is largely powered by technology with very specific needs. AI systems, for example, require access to huge amounts of data; efficient data flow is to AI systems what gold once was to the global economy. Advancements in machine learning (ML), meanwhile, have transformed vast data lakes into actionable insights leveraged by countless businesses and governments.
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At the heart of this ongoing transformation are GPUs, chips that perform mathematical calculations at warp speed to compute various tasks, from deep learning and graphics rendering to video editing and app deployment. A quarter-century ago, GPUs were synonymous with video games; now they’re the key ingredient of our AI-driven world.
As engines powering the AI revolution, GPUs enable the scalability and effectiveness of AI and Big Data applications. However, as with historic gold rushes and oil crises, growing demand for GPU computing is leading to significant challenges.
The use of GPUs in large-scale AI apps and supercomputers, for example, has led to major shortages and exorbitant prices, a trend accelerated by the pandemic. Compounding the problem has been bubbling geopolitical tensions, most notably between the US and China over control of advanced chips designed by Silicon Valley bellwethers like Nvidia and Apple. With both nations hellbent on controlling the future of computing and AI, the economic and political implications of GPU scarcity have somewhat terrifying historical precedents.
The Critical Role of GPU Compute in AI
It’s not just nations who’re jostling for GPU compute dominance. Investors, startups, conglomerates and research organizations are also going to extraordinary lengths to acquire the chips needed to power their products. While tech firms usually save themselves the trouble of sourcing their own by accessing the hardware of cloud computing giants like AWS and Microsoft Azure, the cost is steep – and long wait lists have become the norm.
Into this milieu comes io.net, a Web3 venture committed to provisioning “unmatched compute power for large-scale AI startups.” io.net achieves this by maximizing the utilization of existing GPU resources on the market, enabling users to save up to 90% on compute costs and offering them the ability to deploy GPU clusters in a matter of seconds.
The platform’s recent development of a decentralized physical infrastructure (DePIN) network is a major milestone. Effectively, the network allows GPU computing providers to connect and contribute their resources. Thus, the network sources GPU computing power from a wide range of sources – data centers, crypto miners, and decentralized storage providers – which clients can then leverage to power their apps and products.
GPU providers, of course, don’t do this out of the goodness of their heart: io.net utilizes Solana’s high-throughput blockchain to facilitate swift payments to those who plug into the network.
Conclusion
With global spending on AI-focused chips set to double to $106 billion over the next four years, GPU compute is fast becoming an asset akin to rare earth minerals or precious metals.
As various players and innovators strive to secure access to the computing power they desperately need, innovative solutions for procuring and better utilizing high-performance GPUs will continue to emerge. Let’s hope such solutions play their part to avert the grim outcomes historically associated with control of resources.
