15 million seed round financing, from image generation to AI reasoning solutions, read Prodia in three minutes.
By Chandler, Foresight News
Recently, Prodia, a distributed GPU network for AI reasoning solutions, completed a $15 million financing round led by Dragonfly Capital, with participation from HashKey, Web3.com, Index Ventures, Symbolic Capital, OKX Ventures, and angel investors Balaji Srinivasan, Sandeep Nailwal (founder of Polygon), and Matthew Roszak (co-founder of Bloq).
Most of the core team members of Prodia come from the cloud storage platform Storj. Founder Monty Anderson was the R&D director of Storj Labs, CEO Shawn Wilkinson is the founder and CSO of Storj, and Chief Engineer Monty Anderson was the senior software engineer and R&D director of Storj. So, how did Prodia win the favor of multiple institutions and investors in a highly competitive environment?
What is Prodia?
Prodia is an AI platform that specializes in image and music generation through its API. The company has developed a Stable Diffusion API tool that has many models to convert text into high-quality images, including SD1.5, SDXL, and SD3, each offering different resolutions and features. Prodia's infrastructure has more than 10,000 GPUs capable of processing image generation requests within 2 seconds. Since its inception, Prodia has generated more than 400 million images.
Prodia was founded in 2022 by Shawn Wilkinson, Mikhail Avady and Monty Anderson. CEO Shawn Wilkinson has extensive experience in distributed systems and cloud storage, and founded the decentralized cloud storage company Storj. In 2012, Shawn Wilkinson mined half a bitcoin a day in his dormitory, but eventually shut down the activity due to room temperature issues. However, this also inspired his passion for blockchain technology, and he later participated in a series of early projects in the field of distributed computing. Mikhail Avady and Monty Anderson also have some experience in AI and blockchain.
In 2020, Shawn and co-founder Mikhail Avady became one of the early users of GPT-3 and developed an application for generating music. However, the high cost of GPUs became a bottleneck. So they built a distributed computing layer that not only reduced costs by 50%-90%, but also increased performance by two to four times and was easier to scale. This success led to the birth of Prodia, which focuses on providing scalable AI infrastructure for applications and companies, especially for computationally intensive tasks such as image and video generation.
From image generation to AI inference solutions
Prodia is currently targeting small to large businesses, especially those that need to perform a lot of inference calculations. The current main focus is image generation, but Prodia is expanding its capabilities to gradually get involved in video, text and other formats. The goal of the project is to simplify the expansion process of AI applications, which is seen as the core technology that drives everything, but must become simpler, faster and more affordable. Prodia's solution eliminates the burden of infrastructure management, allowing developers to focus on product features without worrying about capacity issues or compromising with large cloud providers such as Amazon. Currently, Prodia's main business is concentrated on image generation. Next, Prodia will expand into the video field. Prodia pays special attention to high-demand areas, as evidenced by the large-scale use of platforms such as MidJourney and Stable Diffusion. Prodia believes that these are all good starting points for users to cut into AI applications.
Shawn Wilkinson believes that "in the next decade, there will be a trend of comprehensive integration, from software to hardware, from artificial intelligence to blockchain, these technologies will be seamlessly integrated. Artificial intelligence will become an indispensable part of life like smartphones. Everyone will benefit from the advancement of AI technology, and all aspects of life will be improved."
Future development focus
After releasing the new API and obtaining financing, Prodia's main focus from now until the end of the year will be on further expanding its AI models and solutions, with a focus on expanding to the field of video generation in the future. The computing requirements for video generation are 300 to 500 times that of image generation. Prodia's distributed system approach distributes part of the computing work on a wider GPU cloud to solve some of the needs of developers and extension personnel. Prodia's vision is to create a community-driven system controlled by users rather than large cloud providers. Users can run AI generation tasks and use their own computing power to participate in the GPU cloud. Such a system not only provides flexibility and powerful computing power, but also reduces costs.
However, if you want to achieve this standard, you need to ensure that an efficient network topology can ensure that data and tasks can be quickly transmitted and processed between distributed nodes. A unified communication protocol is the basis for seamless collaboration and task scheduling between nodes. In order to improve the reliability and fault tolerance of the system, fault-tolerant mechanisms and data redundancy backup strategies must be designed to ensure that task data and calculation results are not lost even if some nodes fail. At the same time, performance optimization and scalability as well as user experience and availability are equally important. Optimizing the allocation and execution of computing tasks can ensure that the system can efficiently utilize the computing power of all participating nodes. The system architecture must have good scalability to be able to flexibly expand according to the number of participating nodes and the amount of tasks. In terms of user experience, developing easy-to-use interfaces and tools, as well as providing sufficient technical support and documentation, can help users smoothly participate in the system and run AI-generated tasks.
Conclusion
As an emerging force in the AI ââfield, Prodia is expanding with its innovative distributed computing technology and strong generation capabilities, but the rapid iteration of technology and the dynamic changes in the market mean that Prodia must remain flexible and forward-looking to cope with future uncertainties. In general, when a large number of GPUs are required for training, large cloud providers are still a good choice, but for long-running applications, Prodia provides a more sustainable and cost-effective model. Whether it can maintain a certain position in the highly competitive AI red ocean market remains to be seen.
