Imagine a world where the vast, untapped power of consumer-grade GPUs is harnessed to fuel the burgeoning demands of AI, transforming idle computing power into a thriving, decentralized marketplace. This is not a distant dream, but a rapidly unfolding reality, where decentralized physical infrastructure networks (DePINs) are emerging as the unsung heroes in the AI revolution. As AI continues to reshape industries and redefine our daily lives, the insatiable hunger for computational resources has hit a critical point. Enter the world of DePINs, where every underutilized GPU, from the gaming rig in your neighbor’s house to the high-end processors in data centers, joins forces to create a robust, cost-effective alternative to traditional cloud computing. This paradigm shift is not just about meeting demand; it’s a story of innovation, community-driven solutions, and the democratization of AI’s foundational resource: compute power.
The mainstreaming of AI means computing power is at a premium. Decentralized Physical Infrastructure Networks (DePINs) can fill a gap, writes Shayon Sengupta, at Multicoin Capital.
Developments in generative AI over the last 12 months have begun to transform the way people live and work. Language models are being used to develop legal strategies in court cases; image diffusion models are being used to augment the workflows of major entertainment studios; and computer vision advancements have brought fleets of self-driving cars on roads en-masse.
Datacenter revenues and AI workloads
It is well understood that the primary bottleneck to scaling these systems is access to compute resources. Wait times and hourly rates for spot instances of Nvidia’s A100 and H100 chips have consistently trended upward throughout 2023, and chip production capacity simply cannot keep up with demand. The ongoing shortage of graphics cards stems from a perfect storm of materials constraints, supply chain disruptions, surging demand, geopolitical tensions, and the long production cycles inherent in fabricating complex GPUs. Additionally, key materials, like the advanced silicon used in GPU chips, specialized substrates for the PCBs, and memory chips, are all also facing shortages amid supply and demand imbalances.
Datacenter revenues associated with AI workloads were approximately $100 billion in 2023. Datacenters require tremendous upfront capex in the form of land, electricity, and enterprise-grade hardware. New data centers rely on external financing for setup and operation, but rates are high and capital is tight. AI models are increasing in size and complexity, and although the price per unit of computational performance halves every thirty months, AI-specific compute requirements double every six months. Demand is on track to increase orders of magnitude faster than supply.
This is something that investors dream about: a tectonic shift in innovation that impacts practically every business overnight, driven by a finite resource and skyrocketing demand, causing commodity prices to spike. NVIDIA’s YTD returns of 231.5% over the last 12 months are a perfect proxy for this — but even that fails to represent the opportunity at hand. We’re still in the early innings of the AI renaissance. Every Fortune 500 company is figuring out their AI strategy right now, and the demand we see today is nowhere close to demand we’ll see tomorrow. AI will augment and displace workforces, drive productivity, and fundamentally reshape how businesses operate. Compute is the new oil.
GPUs are the currency of AI, and DePINs are here to deliver it
There is an answer to the increasingly vast compute shortage problem: find un-utilized supply.
Decentralized Physical Infrastructure Networks As Resource Solutions
A new form of crypto network, called “Decentralized Physical Infrastructure Networks,” or “DePINs” for short, is coming to the rescue. It is estimated that there are 1.5 billion freely available consumer GPUs globally, and another six million datacenter GPUs in datacenters deployed worldwide outside of the hyperscalers (AWS, GCP, Azure, Oracle). Consumer hardware cards often have comparable computational throughput to enterprise grade cards.
For example, the consumer-grade RTX 3090 is capable of 83 FP32 TFLOPS whereas enterprise-grade A100’s only have 19.5 FP32 TFLOPS. Currently, there are over 330 million consumer-grade GPUs in personal computers (gamers, designers, video editors, etc.) and datacenters that could be brought online. The problem is, it historically hasn’t been possible to incentivize or coordinate these disparate GPUs into usable clusters.
Recently, specialized, AI-focused DePINs, such as Render Network and IO.net, have solved this problem. First, they are giving latent GPU operators incentives to contribute their resources to a shared network in exchange for rewards. Second, they are creating a decentralized networking layer that represents disparate GPUs as clusters AI developers can use. These decentralized compute marketplaces now offer hundreds of thousands of compute resources of varying types, creating a new avenue to distribute AI workloads across a previously unavailable cohort of qualified hardware.
In addition to creating net new GPU supply, DePIN networks are often significantly cheaper – up to 90% cheaper – than traditional cloud providers. They achieve these costs by outsourcing GPU coordination and overhead to the blockchain. Cloud providers markup infrastructure costs because they have employee expenses, hardware maintenance, and datacenter overhead. DePIN networks have none of those expenses, thus they can pass along compute costs practically at cost (with insignificant network coordination fees on top) to end customers.
Looking to the year ahead, we expect these decentralized networks to emerge as one of the key players in the AI race. There are simply not enough GPUs (much less affordable GPUs) right now to service the demand of every major company in the world.
GPUs are the currency of AI, and DePINs are here to deliver it.
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