Decentralized GPU Networks: A New Era of Scalable and Affordable AI – Insights from Nosana

The rapid advancement of artificial intelligence (AI) relies heavily on access to powerful computing resources. In much the same way our brains need oxygen to function, AI systems require vast computational power to operate effectively and improve. Traditionally, cloud platforms such as AWS, Google Cloud, or in-house GPUs and TPUs have been the go-to solutions for AI developers. While these options offer flexibility and scalability, they often come at a high cost and pose technical challenges, particularly for startups.

A promising alternative is emerging in the form of decentralized GPU networks, which promise to democratize access to AI computing power, making it more affordable and accessible. Nosana, a decentralized GPU marketplace that launched in January 2025, is at the forefront of this innovation. Sjoerd Dijkstra, the co-founder of Nosana, shared insights into how decentralized computing networks are revolutionizing AI development during a recent live AMA (Ask Me Anything) session.

Decentralized GPU Networks: A Game-Changer for AI

Nosana's platform connects developers with a decentralized network of GPU owners, enabling them to access powerful computing resources for training and inference tasks without the heavy financial burden typically associated with cloud-based solutions. The platform allows GPU owners to rent out their idle hardware, tapping into underutilized resources worldwide.

“We’re tapping into underutilized resources around the world, making it much more affordable for companies and developers who need scalability without breaking the bank,” said Dijkstra. He also highlighted scalability as a key advantage of decentralized networks, noting that by processing data closer to its source, Nosana can reduce delays and speed up AI testing.

The potential applications for decentralized GPU networks are vast. Dijkstra emphasized that these networks are particularly effective for AI inference tasks. In the Web3 space, decentralized GPUs are already benefiting platforms with large language models (LLMs) and image generation systems, enabling them to handle the increased computational demands of these technologies. Beyond Web3, Dijkstra sees promising applications in industries such as healthcare (for drug discovery and diagnostics), automotive (for autonomous vehicles), finance, and AI agents.

Streamlining AI Development with Pre-Built Models

Building AI models from scratch can be an incredibly time-consuming and resource-intensive process. To address this challenge, Nosana offers a collection of pre-built AI models and templates tailored to common industry needs. These templates, optimized for various use cases, allow businesses to jump-start their AI development without the need to build everything from the ground up.

“Businesses can use our templates as a starting point, tailored to common industry needs,” Dijkstra explained. “These are state-of-the-art AI solutions that are already optimized for your specific use case.”

Several active projects are already leveraging Nosana's resources. For example, Sogni AI, a platform in the creative industry, has successfully reduced costs and scaled operations with the help of Nosana’s decentralized network. Ocada, a platform using AI agents, has integrated Nosana’s computing power to optimize its application layer. Additionally, AlphaNeural, a decentralized marketplace for AI models and datasets, has accelerated its model deployment using Nosana’s infrastructure.

A Global and Enterprise-Ready Network

Nosana's decentralized network is supported by a mix of independent GPU owners and larger providers. Approximately 75% of the GPUs in the network come from individual contributors around the world, while the remainder is sourced from established providers like Render and PikNick. This combination enhances the security, reliability, and performance of Nosana’s platform.

“They make Nosana’s network not only decentralized but also enterprise-ready, which allows us to serve a broader range of clients, from startups to larger organizations that need robust and compliant compute solutions,” said Dijkstra.

To ensure trust and security within the network, GPU hosts on Nosana are required to stake $NOS tokens. This ensures that only trusted participants contribute computing power to the platform. In return, GPU hosts receive incentives and performance-based bonuses.

Paving the Way for a More Accessible AI Future

Looking ahead, Dijkstra believes the decentralization of AI computing power is key to making AI more accessible to a broader range of developers and businesses. However, he also pointed out several important considerations for the future of decentralized AI networks.

“We’re just scratching the surface of the AI revolution, and decentralization is key to making AI more accessible,” Dijkstra concluded. “But we should keep some important points in mind—ensuring security and trust, setting standardized protocols and APIs, and using scalable solutions. That’s what we’re doing at Nosana. By addressing these challenges, we can pave the way for a more accessible and efficient AI landscape.”

As the demand for AI continues to grow, decentralized GPU networks like Nosana are poised to play a crucial role in ensuring that businesses of all sizes have access to the computational power they need to innovate and scale. By leveraging underutilized resources around the world, Nosana is helping to lower the barriers to AI development, enabling a new generation of innovators to push the boundaries of what’s possible.