JAKARTA - Nasdaq-listed company Iris Energy has purchased 248 units of Nvidia H100 GPUs for USD 10 million (IDR 152 billion) in a bid to explore opportunities in the field of generative AI, apart from its core business focus on Bitcoin mining (BTC).
The company expects to take delivery of 248 GPU units in the coming months and plans to use the hardware in cloud computing services.
Iris Energy co-founder and co-CEO Daniel Roberts said the company wanted to leverage existing data centers to serve AI generative computing needs.
"We believe the demand for continuous computing is unlikely to disappear, and we feel we are in a unique position to capture sustained growth in the wider industry; be it ASICs for Bitcoin mining, or GPUs for generative AI and so on," said Roberts, Cointelegraph quoted.
Iris Energy operates in areas with an abundant supply of renewable energy, including wind, solar, and hydro energy, and has set up its modular data centers close to low-cost renewable energy sources that can be monetized for Bitcoins.
According to the Iris Energy website, the company owns four major mining data center facilities, including Canal Flats, Mackenzie, and Prince George in British Columbia, Canada, and the Childress site in Texas.
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Renewable energy-based 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 at overflow power from the nearby Porjus Hydroelectric Station.
Meanwhile, Blockstream recently announced its intention to raise up to $50 million in official investment notes to buy, hold, and then sell BTC mining hardware ahead of the next Bitcoin halving event in 2024.
GPU hardware maker Nvidia has also benefited greatly from the increase in AI-based tools and AI computing, with its total market capitalization exceeding USD 1 trillion (IDR 15,200 trillion) in May 2023.
Nvidia also recently released a leak regarding its next-generation superchip, the GH200 Grace Hopper, which it claims is capable of processing complex generative AI workloads, including large language models, recommendation systems, and vector databases.
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