Nasdaq-listed Iris Energy has bought 248 state of the art Nvidia H100 GPUs for $10 million as it looks to explore opportunities in generative AI in addition to its core business focus on Bitcoin mining.
The firm expects to receive delivery of the 248 GPUs in the coming months and plans to deploy the hardware to serve opportunities in cloud computing.
Iris Energy co-founder and co-CEO Daniel Roberts said the company was looking to leverage its existing data centers into serving generative AI computing requirements.“We believe demand for sustainable computing is unlikely to go away, and feel we are uniquely positioned to capture ongoing growth in the broader industry; whether that be ASICs for Bitcoin mining, or GPUs for generative AI and beyond.”
Iris Energy operates in regions that have an abundance of renewable energy including wind, solar, hydro and has set up its modular data centers nearby to the source of low-cost excess renewable energy to be monetized for Bitcoin.Nvidia’s H100 Tensor Core GPU. Source: Nvidia.com.
According to the Iris Energy website, it has four major data center mining facilities, including Canal Flats, Mackenzie and Prince George in Canada’s British Columbia as well as its Childress site in Texas.
Renewable-powered Bitcoin mining operations continue to attract investment, with Genesis Digital Assets Limited opening a new data centre in Sweden in August 2023 that will operate off abundant power from the nearby Porjus Hydroelectric Power Station.
Meanwhile Blockstream recently announced its intent to raise up to $50 million in an official investment note to purchase, store and then sell BTC mining hardware ahead of Bitcoin’s next halving event in 2024.
GPU hardware manufacturer Nvidia has also seen significant windfall from the rise of AI-powered tools and AI computing, with its total market capitalization eclipsing $1 trillion in May 2023.
Nvidia also recently teased its next-generation GH200 Grace Hopper Superchip which is touted to be able to process complex generative AI workloads, includling large language models, recommender systems and vector databases.
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