Decentralized GPU networks present themselves as a lower-cost layer for running AI workloads, while training of the latest models remains concentrated in hyperscale data centers.
Frontier AI training involves building the largest and most advanced systems, a process that requires thousands of GPUs to operate in close synchronization.
This level of coordination makes decentralized networks impractical for high-end AI training, where the latency and reliability of the Internet cannot match the tightly coupled hardware of centralized data centers.
Most production AI workloads don’t resemble large-scale model training, which opens up space for decentralized networks to handle inference and day-to-day tasks.
“What we’re starting to see is that a lot of open source and other models are becoming compact enough and optimized enough to run very efficiently on consumer GPUs,” Mitch Liu, co-founder and CEO of Theta Network, told Cointelegraph. “This creates a shift towards more efficient open source models and more cost-effective processing approaches.”
From cutting-edge AI training to everyday inference
Advanced training is concentrated among a few large-scale operators because managing large training tasks is costly and complex. The latest AI hardware, like Nvidia’s Vera Rubin, is designed to optimize performance in integrated data center environments.
“You can think of training frontier AI models like building a skyscraper,” Nökkvi Dan Ellidason, CEO of infrastructure company Ovia Systems (formerly Gaimin), told Cointelegraph. “In a centralized data center, all workers are on the same scaffolding and pass the bricks by hand. »
This level of integration leaves little room for the loose coordination and variable latency typical of distributed networks.
“To build the same skyscraper (in a decentralized network), they have to send each brick to each other over the open Internet, which is very inefficient,” Ellidason continued.
Meta trained its Llama 4 AI model using a cluster of over 100,000 Nvidia H100 GPUs. OpenAI doesn’t disclose the size of GPU clusters used to train its models, but infrastructure manager Anuj Sahara said GPT-5 launched with support for more than 200,000 GPUs, without specifying how much of that capacity was used for training versus inference or other workloads.
Inference refers to running trained models to generate answers for users and applications. Ellidason said the AI market has reached a “tipping point in inference.” While training dominated GPU demand as recently as 2024, he estimates that up to 70% of demand will depend on inference, agent, and prediction workloads in 2026.
“This transformed the calculation from a research cost to an ongoing and evolving public service cost,” Ellidason said. “So the demand multiplier through the inner loops makes decentralized computing a viable option in the hybrid computing conversation.”
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Where decentralized GPU networks actually fit
Decentralized GPU networks are best suited for workloads that can be divided, routed, and executed independently, without requiring constant synchronization between machines.
“Inference is a volume business, and it scales with each deployed model and agent loop,” Evgeny Ponomarev, co-founder of decentralized computing platform Fluence, told Cointelegraph. “This is where cost, elasticity and geographic distribution matter more than perfect interconnections. »
In practice, this makes decentralized and gaming-grade GPUs in consumer environments better suited to production workloads that prioritize throughput and flexibility over tight coordination.
“Consumer GPUs, with lower VRAM and home internet connections, don’t make sense for training or very latency-sensitive workloads,” Bob Miles, CEO of Salad Technologies – an aggregator of idle consumer GPUs – told Cointelegraph.
“Today, they are more suited to AI drug discovery, text-to-image/video conversion, and large-scale data processing pipelines – for any cost-sensitive workload, consumer GPUs excel on price and performance.”
Decentralized GPU networks are also well suited for tasks such as collecting, cleaning, and preparing data for model training. Such tasks often require broad access to the open web and can be performed in parallel without close coordination.
This type of work is difficult to run efficiently in hyperscale data centers without extensive proxy infrastructure, Miles said.
When serving users around the world, a decentralized model can have a geographic advantage because it can reduce the distances requests must travel and multiple network hops before reaching a data center, which can increase latency.
“In a decentralized model, GPUs are distributed across many locations around the world, often much closer to end users. As a result, the latency between the user and the GPU can be significantly lower than routing traffic to a centralized data center,” said Theta Network’s Liu.
Theta Network faces a lawsuit filed in Los Angeles in December 2025 by two former employees alleging fraud and token manipulation. Liu said he could not comment on the case because it was ongoing. Theta has previously denied these allegations.
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A complementary layer in AI computing
Frontier AI training will remain centralized for the foreseeable future, but AI computing is moving toward inference, agent, and production workloads that require looser coordination. These workloads reward cost-effectiveness, geographic distribution, and elasticity.
“This cycle has seen the emergence of many open source models that are not system-scale like ChatGPT, but are nonetheless capable enough to run on GPU-equipped personal computers like the RTX 4090 or 5090,” Jieyi Long, co-founder of Liu and CTO of Theta, told Cointelegraph.
With this level of hardware, users can run broadcast models, 3D reconstruction models, and other meaningful workloads locally, creating an opportunity for retail users to share their GPU resources, according to Long.
Decentralized GPU networks do not replace hyperscalers, but they become a complementary layer.
As consumer hardware becomes more capable and open source models become more efficient, a growing class of AI tasks can move outside of centralized data centers, allowing decentralized models to integrate into the AI stack.
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