Nvidia GPU Shortage Fuel Decentralized AI Infrastructure Race

Key Insights:

• Decentralized AI infrastructure is drawing more attention as demand for AI computing power keeps rising and GPUs remain in short supply.
• Eigen, io.net, and Akash are taking different approaches to connect unused computing resources with AI developers who need them.
• Access to GPUs is becoming increasingly important as demand for AI workloads continues to grow.

The race to build decentralized AI infrastructure is getting crowded. New projects are entering the space, all chasing the same goal: making AI compute cheaper, more accessible, and less dependent on a few major providers.

Generative AI demand has taken off quickly, putting pressure on traditional cloud providers. GPU shortages, higher costs, and limited capacity are making it harder for the current system to keep up.

That's creating an opportunity for decentralized networks, which use computing power from many different sources rather than relying mainly on large data centers.

In late 2024, Nvidia said demand for its GPUs was expected to outpace supply for several quarters in fiscal 2026.

Companies dealing with long wait times and expensive cloud bills are starting to look at decentralized AI infrastructure as another option.

Blockchain-based compute networks now promise to tap idle GPUs in data centers, homes, and devices worldwide.

Three projects at the forefront of this trend, Eigen Labs’ Darkbloom, Solana-based io.net, and Akash Network, are racing to address the GPU crunch with novel models of distributed compute.

Eigen’s Darkbloom: turning Macs into AI Nodes

Eigen Labs this spring launched Darkbloom, a “private inference network” that stitches idle Apple Silicon Macs into an AI cloud.

Darkbloom piggybacks on Apple’s hardware security to ensure privacy: inference tasks run in Apple’s Private Cloud Compute environment on each Mac, so even owners cannot see the data.

According to Eigen’s own announcements, the system has already processed “600M+ tokens” with “250 live providers at peak” as it moved to public alpha.

This means hundreds of Mac users around the world can now rent out spare GPU capacity to run AI models. Eigen notes that pricing on Darkbloom is roughly 50% below typical API rates, making it a low-cost AI inference option.

By turning consumer hardware into verifiable AI infrastructure, Darkbloom exemplifies how decentralized AI infrastructure can expand capacity without building new data centers.

io.net: aggregating thousands of GPUs

Another player, io.net, has built what it calls the “GPU aggregation layer” on Solana. In its 2025 year-in-review, io.net said it had brought together 2,752 verified GPUs and 80,000 CPUs across 138 countries.

The network combines unused GPU power from miners, data centers, and other providers, allowing users to access clusters with chips like H100s, A100s, and RTX 4090s when they need them.

Crucially, io.net says it can offer that capacity at far lower cost: “Data from the project suggests that io.net’s pricing is at least 50% cheaper than its centralized competitors."

This means machine learning engineers can rent GPU hours for a fraction of public cloud rates. io.net’s on-chain metrics and audits report rapid growth in both supply and usage, reflecting the urgency of finding new decentralized AI infrastructure amid tight GPU markets.

Akash Network: from Cloud to home GPUs

Akash Network, an early decentralized cloud, is also doubling down on inference. In Q1 2026, it opened Homenode Beta, a program to enlist consumer GPUs in its network.

Individual owners can now register high-end cards, including NVIDIA RTX 4090, 5090 and Quadro RTX 6000 Ada—and earn fees from AI workloads.

Akash CEO Greg Osuri stresses this is partly about resilience: distributing compute across home networks avoids “single point of failure” risks if data centers go offline.

On the demand side, Akash’s managed service AkashML has seen rapid adoption. The network reports that AkashML is “processing 1.7 billion tokens per day on OpenRouter,” already surpassing Cloudflare in daily usage.

In other words, many developers are routing inference jobs through Akash’s decentralized supercloud. The Akash team notes that every transaction on their cloud “drives demand” for its AKT token, binding compute usage to network economics.

The path forward for Decentralized AI Infrastructure

Industry observers note that tight GPU supply and rising energy costs have created an opening for tokenized compute networks.

As Reuters noted, Nvidia and other suppliers face demand far outpacing capacity. By contrast, decentralized projects like Eigen, io.net and Akash can tap underused hardware everywhere.

Eigen’s Darkbloom leverages consumer Macs to add verifiable AI inferencing; io.net aggregates global GPU capacity into a single marketplace; and Akash blends enterprise GPUs with home PCs to boost resilience.

Each approach underscores the appeal of decentralized AI infrastructure: cheaper, more distributed compute that scales with demand.

Together, these efforts suggest a broad “battle” among blockchain-based clouds to prove they can shoulder the AI boom. For now, they are largely still in pilot or alpha stages, and earnings remain modest.

But with Nvidia warning of chip shortages and the cloud crowding under load, projects that reliably deliver cheap GPU hours and verifiable inference are poised to grow.