RETRANSMISSION: HIVE's Paraguay AI Infrastructure Performance Validated in Columbia University Study, Research Heads to NeurIPS

About this update from Hive Digital Technologies Ltd

RETRANSMISSION: HIVE's Paraguay AI Infrastructure Performance Validated in Columbia University Study, Research Heads to NeurIPSThis news release constitutes a "designated news release" for the purposes of the Company's amended and restated prospectus supplement dated June 16, 2026 to its short form base shelf prospectus dated October 31, 2025.San Antonio, Texas--(Newsfile Corp. - June 22, 2026) - HIVE Digital Technologies Ltd. (TSX: HIVE) (NASDAQ: HIVE) (FSE: YO0) (BVC: HIVECO) (the "Company" or "HIVE"), today announces the successful completion of its inaugural research project using HIVE GPUs for AI research purposes in Asunción, Paraguay, in collaboration with the Department of Industrial Engineering and Operations Research at Columbia University in New York. This research has been submitted to The Conference on Neural Information Processing Systems ("NeurIPS"), one of the leading machine learning and computational neuroscience conference held annually in December. Along with ICLR and ICML, it is one of the three primary conferences of high impact in machine learning and artificial intelligence research globally. This establishes a proof of concept for intercontinental AI training, where researchers in New York City successfully ran iterative training runs on GPUs located in Asunción, Paraguay. With this data, HIVE now has a reference point for the performance of AI workloads using GPUs in Asunción. Using code optimizations developed by the Columbia team, the research found that HIVE's A40 GPUs matched the performance of newer-generation H100 GPUs.A researcher from the Department of Industrial Engineering and Operations Research at Columbia University said: "We study neural network pretraining using optimization theory over general geometry and under large noise. We design and analyze an accelerated algorithm that matches the performance of the current leading method, Muon, in both theory and practice.As shown in the paper, our work on these nodes focuses on the performance of pretraining algorithms. Over the past two months, we optimized our code for the A40s and tested the throughput and latency of Muon and our variants. In our use case of pretraining LLMs of up to 1.4B parameters, our results match those of H100s after normalizing for each hardware's raw performance.Additionally, we tested the serving throughput and latency of our 1.4...

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