The rapidly growing fields of Artificial Intelligence, Graphics Processing, and the Metaverse create an insatiable demand for powerful and affordable computing capabilities. However, many data centers globally are operating well below their capacity, with the US averaging only 12-18% utilization. This inefficiency results in bottlenecks that drive up the cost of GPU computing, making it prohibitively expensive for many businesses. To address this issue, various initiatives are underway to create a decentralized cloud network that harnesses these underutilized resources, prioritizing AI-related industries. We will explore some of the most promising projects tackling this challenge.
AI Model Training and Inference: Understanding Compute Power Utilization
Compute power is primarily used in two main stages of artificial intelligence (AI) development: model training and inference.
Model Training: This stage involves employing a machine learning algorithm to build a model using a training dataset. The goal is to create a model that can learn from the data and make predictions or decisions based on it.
Inference: Once the model is trained, it can be applied to new datasets to generate outputs or "predictions." This stage is critical for understanding new data and making real-time decisions.
1. Akash Network: Decentralized Cloud Computing
Akash Network is a revolutionary open-source supercloud network that establishes a decentralized marketplace for cloud computing. Facilitating secure and efficient buying and selling of computing resources, the network employs a unique "reverse auction" system, allowing customers to submit their preferred prices while providers compete for their business. Leveraging technologies like Kubernetes and Cosmos and utilizing the Akash Token (AKT) for transactions and governance, Akash Network offers features such as limitless storage, dedicated IP addresses, and robust privacy and security.
2. Spheron Network: Decentralized Cloud Computing
Spheron Network is a decentralized computing platform that strives for fairness, making distributed resources accessible to all. Spheron Compute presents a robust and cost-effective alternative to centralized cloud services, priced at just one-third of the traditional cost. The goal is to democratize public cloud access, offering a more sustainable model for computing. The Spheron platform allows organizations and developers to deploy, run, and scale based on their needs, free from the limitations of centralized cloud environments.
Spheron satisfies decentralized application requirements: Private images, Auto scale instance, Scale on demand, Real-time instance metrics, Faster GPUs, Free Bandwidths, Terraform Providers and SDKs, Instance health checks, activity, shell access and more. Spheron provides add-on storage solutions for long-term data storage and edge bandwidth acceleration through its global CDN. With Spheron, you can easily set up your nodes in just a few minutes and enjoy low maintenance and operations costs and a great developer experience.
Spheron has recently introduced a series of groundbreaking updates that have significantly enhanced our platform's capabilities! Spheron has scaled up its computing power in the US-East and West regions from 256 to over 1000 CPUs. Plus, they have expanded their global reach by adding a new APAC region in Singapore. This means you can enjoy faster and more efficient computations no matter where you are.
3. Gensyn: Global Supercluster for ML Computing
Gensyn is a machine learning computing network uniting global computational resources into an accessible supercluster. Enabling developers to train deep learning models over a network of interconnected devices, Gensyn utilizes blockchain technology for task validation and token-based payments. With a mission to democratize compute power, Gensyn provides AI developers with a cost-effective, scalable, and environmentally conscious solution.
4. Io.net: Decentralized Physical Infrastructure for GPU Computing
Io.net has established a decentralized physical infrastructure network sourcing GPU computing power for AI and machine learning applications. Streamlining infrastructure deployment using an open-source library, Io.net leverages Solana's blockchain for transactions, rewarding contributors with computing power. Offering a more adaptable approach than centralized services, Io.net facilitates the booking of compute resources, analogous to Kayak in the airline industry.
5. Cortex Labs: Blockchain-Integrated AI Platform
Cortex Labs is an open-source, peer-to-peer, decentralized blockchain platform seamlessly integrating AI models. AI developers can upload their models onto the blockchain, incorporating them into smart contracts. Noteworthy achievements include launching the world's first AI on the blockchain and enabling on-chain AI inference. Recognized for innovative AI integration and machine-learning capabilities, Cortex empowers decentralized applications to utilize on-chain inference and achieve smart contract objectives.
6. Bittensor: Decentralized Blockchain-Based ML Network
Bittensor, an open-source protocol, drives a decentralized blockchain-based machine-learning network akin to Bitcoin's mining network. Providing censorship-resistant access to a decentralized network of machine learning models, Bittensor aims to establish a transparent market for AI models. Its focus on incentivizing AI model participation and fostering value-creating markets distinguishes it within the industry.
Conclusion
In conclusion, the rapid growth of AI, graphics processing, and the metaverse has created a significant demand for powerful and affordable computing capabilities. However, many data centers need to be more utilized, resulting in bottlenecks that drive up the cost of GPU computing, making it prohibitively expensive for many businesses. To address this issue, various initiatives are underway to create a decentralized cloud network that harnesses these underutilized resources, prioritizing AI-related industries.
As the race towards a decentralized future continues, we can expect to see even more innovative solutions emerge, further disrupting the status quo and paving the way for a new era of computing that is more equitable, accessible, and sustainable for all.
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