Multicoin Capital: Why We Invested in io.net
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Multicoin Capital: Why We Invested in io.net
If you have spare GPUs, you can also contribute them to the network today and earn points in return.
Navigating Web3 tides with focused insightsAuthor: Multicoin Capital
Translation: TechFlow
We are excited to announce our investment in io.net, the leading distributed computing marketplace for AI workloads. We led the seed round and participated in the Series A financing. In total, io.net has raised $30 million from Multicoin, Hack VC, 6th Man Ventures, Modular Capital, and a group of closely aligned angel investors to make an on-demand, production-ready computing market a reality.
I first met Ahmad Shadid, founder of io.net, in April 2023 at the Austin Solana Hacker House, and was immediately drawn to his unique focus on democratizing access to compute resources for machine learning workloads.
Since then, the io.net team has rapidly executed on this vision. Today, the network aggregates tens of thousands of distributed GPUs and has delivered over 57,000 hours of compute to AI companies. We’re thrilled to partner with them as we enter a decade of AI renaissance.
Global Compute Shortage
Demand for AI compute is growing at an astonishing pace; demand is insatiable. In 2023, data center revenue from AI workloads will exceed $100 billion, yet even under the most conservative estimates, AI demand outstrips chip supply.
During periods of high interest rates and capital scarcity, new data centers capable of housing such hardware require massive upfront investments. The crux lies in production constraints of advanced chips like the NVIDIA A100 and H100. Despite steadily improving GPU performance and declining costs, the physical manufacturing process cannot be accelerated quickly—shortages of raw materials, components, and production capacity limit growth speed.
Despite AI’s immense potential, its physical footprint continues to expand, consuming space, power, and cutting-edge equipment worldwide, straining budgets across the globe. io.net offers a path toward a world unbound by current supply chain limitations.
io.net is a classic example of DePIN: leveraging token incentives to structurally reduce the cost of acquiring and retaining supply-side resources, ultimately lowering costs for end consumers. The network aggregates a vast, heterogeneous supply of GPUs into a shared pool available for AI developers and companies. Today, the network is powered by thousands of GPUs from data centers, mining farms, and consumer devices.
While aggregating these resources is valuable, AI workloads don’t automatically scale from centralized enterprise-grade hardware to distributed networks. There have been several attempts throughout crypto history to build distributed computing networks, most of which failed to generate meaningful demand.
Coordinating and scheduling workloads across heterogeneous hardware with varying memory, bandwidth, and storage configurations is far from simple. We believe the io.net team possesses the most practical solution currently available to make such hardware aggregation useful and economically viable for end customers.
Paving the Way for Clusters
In computing history, software frameworks and design patterns evolve according to the hardware configurations available in the market. Most frameworks and libraries used for AI development heavily rely on centralized hardware resources, but over the past decade, significant progress has been made in distributing these workloads across discrete instances of geographically dispersed hardware.
io.net leverages latent hardware already present in the world, deploying custom networking and orchestration layers to create and bring online a super-scalable GPU internet. The network integrates various open-source distributed computing frameworks such as Ray, Ludwig, and Kubernetes, enabling machine learning engineering and operations teams to scale their workloads across GPU networks with minimal modifications.
ML teams can launch clusters on-demand and use these libraries to handle coordination, scheduling, fault tolerance, and scaling, allowing parallel processing of workloads across io.net GPUs. For example, if a group of animators contributes their home-based GPUs to the network, io.net can assemble a cluster that makes this collective compute power accessible to image diffusion model developers anywhere in the world.
BC8.ai, a fine-tuned Stable Diffusion model trained entirely on io.net hardware, exemplifies this capability. The io.net browser displays real-time inference results along with payments to network contributors.
Each inference is recorded on-chain to provide provenance. The fee for generating this particular image was distributed to a cluster composed of six RTX 4090s—consumer-grade gaming GPUs.
Today, tens of thousands of devices are connected to the network, spanning mining farms, underutilized data centers, and Render Network consumer nodes. Beyond creating entirely new GPU supply, io.net competes cost-effectively with traditional cloud providers, often offering cheaper resources.
They achieve these cost savings by outsourcing GPU coordination and overhead to the protocol. In contrast, cloud providers price in infrastructure costs due to expenses related to staffing, hardware maintenance, and data center operations. The structural arbitrage cost of consumer GPU clusters and mining farms is significantly lower than the opportunity cost accepted by hyperscale cloud companies, resulting in a structural advantage where dynamic pricing on io.net remains below ever-rising cloud prices.
Building the GPU Internet
io.net enjoys the unique advantage of remaining asset-light, reducing the marginal cost of serving any given customer to nearly zero, while building direct relationships with both demand and supply sides of the market. Thousands of new businesses need GPU access to build competitive products that will one day interact with everyone.
If you have idle GPUs, you can contribute them to the network today and earn credits.
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