Iris Energy buys 248 Nvidia GPUs worth $10M for generative AI and Bitcoin mining

Iris Energy has invested $10 million in the latest generation of Nvidia GPUs to explore generative AI while it continues to focus on Bitcoin mining.

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 artificial intelligence (AI) in addition to its core business focus on Bitcoin (BTC) 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:

Iris Energy operates in regions that have an abundance of renewable energy — including wind, solar and hydro — and has set up its modular data centers nearby to the source of low-cost excess renewable energy to be monetized for Bitcoin.

According to Iris Energy’s 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.

Related: Tether CTO stays silent on Bitcoin mining locations

Renewable-powered Bitcoin mining operations continue to attract investment, with Genesis Digital Assets Limited opening a new data center 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, including large language models, recommender systems and vector databases.

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