Startup demos upcoming decentralized GPU infrastructure network to OpenAI, Uber

A job that began as an institutional-grade quantitative trading system for stocks and cryptocurrencies has actually transitioned to end up being a decentralized network sourcing GPU computing power to serve increasing need for AI and device knowing services.Io.net has established a test network that sources GPU calculating power from a variety of information centers, cryptocurrency miners and decentralized storage service providers. Aggregating GPU computational power is promoted to dramatically decrease the cost of renting these sources that are ending up being significantly expensive as AI and artificial intelligence advances.Speaking solely to Cointelegraph, CEO and co-founder Ahmad Shadid unloads information of the network that aims to offer a decentralized platform for renting computing power at a fraction of the cost of centralized options that currently exist.Related: Future of payments: Visa to invest $100M in generative AIShadid explains how the project was conceived in late 2022 throughout a Solana hackathon. Io.net was establishing a quantitative trading platform that relied on GPU calculating power for its high-frequency operations, however was hamstrung by the expensive costs of leasing GPU computing capacity.The io.net platform will allow GPU computing suppliers to supply resource to clusters for AI and artificial intelligence needs. Source: io.netThe team unpacks the obstacle of leasing high-performance GPU hardware in its core paperwork, with the price of renting a single NVIDIA A100 balancing around $80 each day per card. Needing more than 50 of these cards to operate 25 days a month would cost more than $100,000. A service was found in the discovery of Ray.io, an open-source library which OpenAI utilized to distribute ChatGPT training throughout over 300,000 GPUs and cpus. The library streamlined the jobs facilities, with its backend established in the short space of two months. Shadid demoed io.nets working testnet at the AI-focused Ray Summit in Sept. 2023, highlighting how the project aggregates calculating power which is served to GPU consumers as clusters to satisfy specific AI or machine knowing usage cases.” Not only does this model allow io.net to provision GPU calculate as much as 90% less expensive than incumbent providers, but it enables for essentially endless computing power.” The decentralized network is set to utilize Solanas blockchain to deliver SOL and USD Coin (USDC) payments to artificial intelligence engineers and miners that are leasing or offering computing power.” When ML engineers pay for their clusters, these funds are directed straight to the miners that served in the cluster with their GPUs, with a small network fee being allocated to the io.net protocol.” The projects roadmap is set to consist of the launch of a dual native token system that will feature $IO and $IOSD. The token model will reward miners for executing machine knowing workloads and maintaining network uptime while thinking about the dollar expense of electrical power usage.” The IO coin will be freely sold the crypto market and is the gate to access the compute power, while the IOSD token will work as a steady credit token algorithmically pegged to 1 USD.” Shadid tells Cointelegraph that io.net basically differs from centralized cloud services like Amazon Web Services (AWS):” To use an analogy, theyre United Airlines and were Kayak; they own planes whereas we assist people book flights.” The creator adds that any organizations that need AI calculation usually utilize third-party service providers, given that they lack the GPUs to handle all of it in-house. With need for GPUs estimated to increase by ten times every 18 months, Hadid states that these is typically insufficient capability to fulfill demand, leading to long haul times and high prices.This is intensified by what he describes as inefficient usage of data centers that are not optimized for the kind of AI and artificial intelligence work that is quickly increasing:” There are thousands of independent datacenters in the US alone, with an average usage rate of 12 – 18%. As a result, bottlenecks are being created, which is having the ripple effect of increasing costs for GPU calculate.” The upside is that the average cryptocurrency miner stands to gain by renting their hardware to compete with the similarity AWS. Hadid states that the typical miner utilizing a 40GB A100 makes $0.52 a day, while AWS is selling the very same card for AI computing for $59.78 a day. “Part of the value proposition of io.net is initially we enable participants to be exposed to the AI compute market and resell their GPUs and for the ML engineers we are considerably cheaper than AWS.” Figures shown Cointelegraph price quote that miners with GPU resources at their disposal might make 1500% more than they would from mining a variety of cryptocurrencies.Magazine: Blockchain investigators: Mt. Gox collapse saw birth of Chainalysis

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A task that began out as an institutional-grade quantitative trading system for cryptocurrencies and stocks has transitioned to end up being a decentralized network sourcing GPU calculating power to serve increasing need for AI and machine knowing services.Io.net has developed a test network that sources GPU calculating power from a range of information centers, cryptocurrency miners and decentralized storage providers. Io.net was developing a quantitative trading platform that relied on GPU computing power for its high-frequency operations, however was hamstrung by the exorbitant costs of renting GPU computing capacity.The io.net platform will enable GPU calculating service providers to offer resource to clusters for AI and device learning requirements. Shadid demoed io.nets working testnet at the AI-focused Ray Summit in Sept. 2023, highlighting how the project aggregates computing power which is served to GPU consumers as clusters to fulfill particular AI or device learning use cases.” Not only does this design allow io.net to arrangement GPU compute up to 90% less expensive than incumbent suppliers, but it allows for essentially endless computing power.