Decentralized GPU networks are touted as a cheaper layer for running AI workloads, while training the latest models remains concentrated in hyperscale data centers.

Frontier AI training involves building the largest and most advanced systems, a process that requires the tight synchronization of thousands of GPUs.

This level of coordination makes decentralized networks impractical for high-end AI training, where the latency and reliability of the Internet cannot match tightly coupled hardware in centralized data centers.

Most AI workloads in production do not resemble training models at scale, opening up space for decentralized networks to handle inference and everyday tasks.

“We are starting to see many open source and other models becoming compact and optimized enough to run very efficiently on consumer GPUs,” Mitch Liu, co-founder and CEO of Theta Network, told Cointelegraph. “This is driving a shift towards more efficient models and more cost-effective, open-source computing approaches.”

From pioneering AI training to everyday reasoning

Pioneer training is focused on a few hyperscale operators because running immense training tasks is high-priced and convoluted. The latest AI hardware, such as Nvidia’s Vera Rubin, is designed to optimize performance in integrated data center environments.

“You can think of training in pioneering AI models as building a skyscraper,” Nökkvi Dan Ellidason, CEO of infrastructure company Ovia Systems (formerly Gaimin), told Cointelegraph. “In a centralized data center, all employees sit on the same scaffold and hand-pass bricks.”

This level of integration leaves little room for the loose coordination and variable latencies typical of distributed networks.

“To build the same skyscraper [in a decentralized network]they have to mail each brick to each other, which is highly inefficient,” Ellidason continued.

Meta trained its Llama 4 AI model uses a cluster of over 100,000 Nvidia H100 GPUs. OpenAI doesn’t reveal the size of the GPU clusters it uses to train its models, but the infrastructure leader is Anuj Saharan he said GPT-5 was launched with support from over 200,000 GPUs, without specifying how much of that capacity was used for training and how much for inference or other workloads.

Inference refers to running trained models to generate responses for users and applications. Ellidason said the artificial intelligence market has reached a “tipping point.” While training dominated GPU demand well into 2024, he estimated that as much as 70% of demand in 2026 would come from inference, agent, and prediction workloads.

“This moved computation from being a research cost to an ongoing, scalable utility cost,” Ellidason said. “Thus, demand multiplier through internal loops makes decentralized computing a viable option in the hybrid computing discussion.”

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Where decentralized GPU networks actually reside

Decentralized GPU networks are best suited for workloads that can be shared, routed, and executed independently without the need for constant synchronization between machines.

“Inference is a mass business that scales with each model and agent loop deployed,” Evgeny Ponomarev, co-founder of decentralized computing platform Fluence, told Cointelegraph. “This is where cost, flexibility and geographic distribution matter more than excellent interconnections.”

In practice, this makes decentralized GPUs designed for consumer gaming environments better suited for production workloads that prioritize throughput and flexibility over tight coordination.

“Consumer GPUs with less VRAM and home internet connections don’t make sense for training or workloads that are very latency sensitive,” 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, and large-scale data processing pipelines – for any workload that is cost-sensitive, consumer GPUs excel in price performance.”

Decentralized GPU networks are also ideal for tasks such as collecting, cleaning, and preparing data for model training. Such tasks often require broad access to an open network and can be performed in parallel without close coordination.

Miles said it’s challenging to do this type of work efficiently in hyperscale data centers without extensive proxy infrastructure.

When serving users around the world, a decentralized model may have a geographic advantage because it can reduce the distances requests must travel and the number of network hops before reaching the data center, which can raise latency.

“In a decentralized model, GPUs are distributed across multiple locations around the world, often much closer to end users. As a result, latency between the user and the GPU can be much lower compared to traffic going to a centralized data center,” said Theta Network’s Liu.

Theta Network is facing a lawsuit filed in Los Angeles in December 2025 by two former employees who allege fraud and token manipulation. Liu said he could not comment on the matter because the case was ongoing. Theta has already done this negative allegations.

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Complementary layer in AI processing

For the foreseeable future, AI training will remain centralized, but AI processing is moving toward inference, agents, and production workloads that require looser coordination. These workloads reward cost efficiency, geographic distribution, and flexibility.

“This cycle has seen the emergence of many open source models that are not at the scale of systems like ChatGPT, but are still powerful enough to run on PCs with GPUs like the RTX 4090 or 5090,” Liu co-founder and Theta CTO Jieyi Long told Cointelegraph.

Long says that with this level of hardware, users can run diffusion models, 3D reconstruction models, and other significant workloads locally, creating an opportunity for retail users to share GPU resources.

Decentralized GPU networks do not replace hyperscalers, but become a complementary layer.

As consumer hardware becomes more powerful and open source models become more productive, an increasing class of AI workloads can be moved beyond centralized data centers, allowing decentralized models to fit into the AI ​​stack.

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