Introduction
The insatiable demand for affordable computing power in burgeoning fields such as AI-generated content, Web3, and the Metaverse has led to the realization that traditional centralized cloud computing models are no longer adequate. This has spurred the development of decentralized cloud networks, which aim to maximize untapped compute resources by leveraging underutilized data centers worldwide.
Understanding AI Model Training and Inference
Compute power is primarily utilized in two fundamental AI processes: training and inference. Training involves employing machine learning algorithms to build models based on training data, while inference involves applying these trained models to new data to generate predictions and real-time decision-making.
Decentralized Cloud Network Projects
Akash Network
- Founded: 2017
- Focus: Training, Inference
- Platform: Open-source supercloud network
- Key Features: Reverse auction system, competitive pricing, high security, limitless storage
Cortex Labs
- Founded: 2017
- Focus: Inference
- Platform: Open-source, peer-to-peer blockchain platform
- Key Features: AI model integration, on-chain inference, smart contracts
Bittensor
- Founded: 2019
- Focus: Inference
- Platform: Open-source protocol
- Key Features: Censorship-resistant AI model network, transparent market for AI models
Gensyn
- Founded: 2020
- Focus: Training
- Platform: Machine learning computing network
- Key Features: Global supercluster, idle computing power utilization, token-based payments
OORT
- Founded: 2021
- Focus: Training, Inference
- Platform: Decentralized data cloud
- Key Features: Global computing and storage resources, enterprise-grade solutions, ‘Talk to Data’ service
Io. net
- Founded: 2022
- Focus: Training, Inference
- Platform: Decentralized physical infrastructure network
- Key Features: OpenAI-inspired library, Solana blockchain for transactions, flexible booking system
Conclusion
The emergence of decentralized cloud networks is a testament to the growing need for efficient and cost-effective compute resources for AI applications. These networks offer a range of features that can address the challenges of traditional centralized cloud computing, including underutilized resources, high costs, and scalability limitations. As decentralized cloud networks continue to evolve, they have the potential to revolutionize the way we access, manage, and utilize compute power for AI and other data-intensive workloads.
Note:
This article is for informational purposes only and does not constitute investment advice.