Distributed GPU networks are touted as a low-cost layer for running AI workloads, but modern model training is still centralized within hyperscale data centers.
Frontier AI training involves building the largest and most advanced systems. This process requires thousands of GPUs to work in tight synchronization.
This level of coordination makes decentralized networks impractical for top-end AI training because the internet’s latency and reliability cannot match the tightly coupled hardware of centralized data centers.
Most AI workloads in production don’t resemble large-scale model training, opening the space for distributed networks to handle inference and mundane tasks.
“What we are starting to see is that many 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 is creating a shift towards more efficient models of open source and more economical processing approaches.”
From cutting-edge AI training to everyday reasoning
Running large-scale training jobs is expensive and complex, so Frontier Training is focused on a small number of hyperscale operators. Modern AI hardware, such as Nvidia’s Vera Rubin, is designed to optimize performance within consolidated data center environments.
“You can think of training a frontier AI model 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 passing bricks by hand.”
This level of integration leaves little room for the loose coordination and variable delays inherent in distributed networks.
“To build the same skyscraper[with a decentralized network]each brick would have to be mailed to each other over the open internet, which is highly inefficient,” Elidason continued.
Meta trained its Llama 4 AI model using a cluster of over 100,000 Nvidia H100 GPUs. OpenAI did not disclose the size of the GPU cluster used to train the models, but infrastructure lead Anuj Saharan said GPT-5 launched with support from more than 200,000 GPUs, without disclosing how much of that capacity was used for training and inference and other workloads.
Inference refers to running a trained model to generate responses for users and applications. Elidason said the AI market has reached an “inference tipping point.” While training dominated GPU demand until 2024, he estimated that up to 70% of demand will come from inference, agent, and prediction workloads by 2026.
“This has transformed computing from a research cost to an ongoing, scalable utility cost,” Elidason said. “Thus, the increased demand from the inner loop makes distributed computing a viable option in the hybrid computing conversation.”
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Where distributed GPU networks really fit
Distributed GPU networks are ideal for workloads that can be partitioned, routed, and executed independently without requiring continuous synchronization between machines.
“Inference is a volume business, and it scales with every model and agent loop that is deployed,” Evgeny Ponomarev, co-founder of distributed computing platform Fluence, told Cointelegraph. “There, cost, resiliency, and geographic spread are more important than perfect interconnection.”
In reality, distributed gaming-grade GPUs in consumer environments are better suited for production workloads that prioritize throughput and flexibility over tight tuning.
“Consumer GPUs don’t make sense for very latency-sensitive training or workloads because they have low VRAM and low home internet connections,” Bob Miles, CEO of Salad Technologies, an aggregator of idle consumer GPUs, told Cointelegraph.
“Today, they are better suited for AI drug discovery, text-to-image/video conversion, and large-scale data processing pipelines. For any workload where cost is a concern, consumer GPUs offer superior price performance.”
Distributed GPU networks are also suitable 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 tight coordination.
According to Miles, it is difficult to perform this type of work efficiently within a hyperscale data center without a large proxy infrastructure.
A distributed model has geographic advantages when serving users around the world. This can reduce the distance a request has to travel and the multiple network hops it takes to reach your data center, which can increase latency.
“In a distributed model, GPUs are distributed across many locations around the world, often very close to the end user. As a result, the latency between the user and the GPU is much lower than when traffic is routed 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 alleging fraud and token manipulation. Liu said he could not comment because the matter was pending. Sita had previously denied the charges.
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Complementary layers of AI computing
Frontier AI training will remain centralized for the foreseeable future, but AI computing is moving to inference, agents, and production workloads that require more gradual coordination. These workloads reward cost efficiency, geographic distribution, and elasticity.
“This cycle has seen the emergence of a number of open source models that, while not reaching the scale of systems like ChatGPT, are capable enough to run on personal computers with GPUs such as the RTX 4090 and 5090,” Jieyi Long, Liu’s co-founder and head of technology at Theta, told Cointelegraph.
According to Long, that level of hardware allows users to run diffusion models, 3D reconstruction models, and other meaningful workloads locally, creating an opportunity for retail users to share GPU resources.
Distributed GPU networks are not replacing hyperscalers, but are becoming a complementary layer.
As consumer hardware becomes more capable and open source models become more efficient, the variety of AI tasks will expand and be able to move outside of centralized data centers, allowing distributed models to fit into the AI stack.
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