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Tech

HIVE Digital surpasses 22 EH/s, speeds up conversion from Tier-1 to Tier-3 data centers for AI Cloud expansion in Sweden

HIVE Digital Technologies (TSX-V:HIVE, NASDAQ:HIVE) announced that it has surpassed 22 Exahash per second (EH/s) in global Bitcoin-mining capacity, marking 267% year-to-date growth, while accelerating the conversion of Tier-1 data centers into Tier-3 high-performance computing (HPC) facilities to expand its AI Cloud footprint in Sweden.

The multinational digital infrastructure company said its current production has reached 9.5 Bitcoin per day with fleet efficiency of about 17.8 Joules per Terahash (J/TH) and a 55% mining margin after electricity costs at $47 Hashprice, demonstrating HIVE's operational efficiency across multiple countries and nine time zones.

"Crossing 22 EH/s marks another major milestone for HIVE as we build one of the world's most efficient and sustainable Bitcoin-mining fleets," HIVE Digital Technologies executive chairman Frank Holmes said in a statement.

"At the same time, our AI strategy accelerates the conversion of Tier-1 data centers into Tier-3 HPC facilities, positioning HIVE as a dual-engine digital-power company that bridges blockchain and the AI super cycle."

HIVE noted that its 22 EH/s milestone reflects the successful expansion of its hydro-powered Valenzuela facility in Paraguay, the company's third 100-megawatt green campus in the country.

The company added that with additional application-specific integrated circuit (ASIC) miners coming online, HIVE expects to reach 25 EH/s by US Thanksgiving, targeting fleet efficiency of 17.5 J/TH.

HIVE said it is targeting 25 EH/s by late 2025 and 35 EH/s during 2026, while scaling its HPC division five-fold over the same period.

HIVE reports EH/s growth as material to revenue and cash-flow expansion, noting that digital-infrastructure companies are typically valued at multiples of revenue and cash flow, according to the company.

HIVE expects to operate about 6,000 graphics processing units (GPUs) by 2026, serving both AI training and inference workloads.

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