io.net is an advanced distributed computing network that provides GPU computing services cheaper, faster, and more flexible than traditional centralized services, offering machine learning engineers access to unlimited computing power.
Author: bymalul / Source
Translation: Plain Blockchain
1. What is io.net?
io.net Cloud is an advanced distributed computing network that allows machine learning engineers to access distributed cloud clusters at lower cost compared to comparable centralized services.
Modern machine learning models often utilize parallel and distributed computing. Harnessing the power of multiple cores across multiple systems is crucial for optimizing performance or scaling to larger datasets and models. The training and inference processes involve coordinated GPU networks that collaborate with each other.
1) What is io.net's mission and what are you striving to achieve?
io.net is designed as a decentralized GPU network to provide unlimited computing power for ML applications. We make computing more scalable, accessible, and efficient. Our mission is to unlock fair access to computing power by combining over a million GPUs from independent data centers, crypto miners, and encryption projects like Filecoin or Render.
2) How is io.net different from AWS?
io.net offers a fundamentally different approach to cloud computing, leveraging a distributed and decentralized model that provides users with more control and flexibility. Our service is permissionless and cost-effective. The combination of these factors sets io.net apart as a unique player in the decentralized provider space.
3) Why is io.net cheaper or faster than other providers like AWS?
io.net's pricing and speed are several orders of magnitude cheaper and faster than current solutions.
By utilizing underutilized resources such as independent data centers, crypto miners, and consumer GPUs, we are able to offer computing costs that are up to 90% cheaper than traditional cloud service providers.
We are also faster because creating a distributed cluster through traditional cloud service providers is a time-consuming process. Companies like AWS often require detailed KYC information, long-term contracts, and typically have waiting lists for the most popular hardware.
As a result, obtaining GPU computing from the cloud typically takes weeks.
On the other hand, io.net does not impose such restrictions, allowing users to access and deploy clusters within 90 seconds.
Ultimately, the combination of speed and cost makes io.net 10 to 20 times more efficient than traditional cloud service providers.
2. What is DePIN and how does it apply to io.net?
DePIN, or Decentralized Physical Infrastructure Network, leverages blockchain, the Internet of Things, and the broader Web3 ecosystem to create, operate, and maintain real-world physical infrastructure. These networks use token incentives to coordinate, reward, and protect network members. io.net is the first and only GPU DePIN. We are optimized for machine learning but applicable to all GPU use cases, as we connect computing providers with users, providing accessibility and profit for all participants.
1) What types of GPUs does io.net provide?
We offer various types of GPUs, including the NVIDIA RTX series and AMD Ryzen series;
We also provide various types of CPUs, including Intel, AMD, and the Apple M2 chip with unparalleled neural engine.
Please refer to the (pricing page) for a complete list of supported GPUs, and if your hardware is not listed, please contact our support team.
Our minimum requirements are:
+12 GB of memory.
+500 GB of available disk space.
Internet speed: download speed +500 MB/s, upload speed +250 Mbps, ping value 30ms.
2) How is io.net used for machine learning?
io.net is natively built on ray.io, a machine learning framework for distributed computing, similar to the framework used by OpenAI to train GPT3 on over 300,000 CPUs and 20,000 GPUs. You can use io.net to distribute your AI and Python applications, covering a wide GPU grid from reinforcement learning to deep learning to tuning, and model serving.
We have pipelined to support all ML engineers' frameworks for their workload distribution, such as Anyscale, Pytorch FSDP, Tensorflow, Predibase, etc.
3. How do we manage the availability and allocation of users on the global GPU network?
io.net connects clients from around the world to a global network of providers. We deploy our containers on every working machine, facilitating the integration and monitoring of io.net's virtual network availability across all devices on the network. Our algorithm intelligently groups resources, matches them with the engineer's selection, and combines them into a cluster, all completed within 90 seconds. Our network solution has been thoroughly tested and proven to be reliable.
1) What are the connection requirements for providers?
We offer different levels of connections for clients, from low to ultra-high. While our absolute minimum connection requirement is 250 mbps, we strongly recommend providers to support at least 1 gbps download and upload speeds to remain attractive to our clients.
We expect an average data flow of 5GB per hour.
2) Can clients create their GPUs more flexibly?
Clients can create their clusters with unprecedented flexibility through a range of choices and options: cluster types categorized by use case, sustainability (e.g., "green GPUs" powered by 100% clean energy), geographical location, security compliance levels (SOC2, HIPAA, end-to-end encryption), connection levels, and cluster purposes (we currently support Ray App, but we are expanding to other use cases). io.net's out-of-the-box configuration requires no additional setup for clients to deploy clusters.
3) What pricing models are available? Are there different pricing tiers based on GPU models/performance?
Prices are automatically determined based on supply and demand; GPU specifications, such as internet speed, GPU manufacturing and model, security/compliance certifications, etc., also influence pricing. For example, a top-tier enterprise-grade GPU with SOC2 compliance and >2 Gbps will be priced higher than a consumer-grade GPU without SOC2 compliance and slower connection speeds.
Source: https://medium.com/@bymalul/what-is-the-io-net-a89973a67821
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