HIVE validates Paraguay AI infrastructure via Columbia study
HIVE Digital Technologies Ltd. and Columbia University have completed a research project demonstrating that HIVE's A40 GPUs in Paraguay can match the performance of H100 GPUs for AI training after software optimizations. The study, submitted to NeurIPS, validates the feasibility of intercontinental AI training and supports HIVE's plans to build an HPC/AI Gigafactory in Yguazú, Paraguay, with a 100 MW substation and a target ready-for-service date in H2 2027.

*this image is generated using AI for illustrative purposes only.
HIVE Digital Technologies Ltd. has announced the successful completion of its inaugural AI research project, conducted in collaboration with the Department of Industrial Engineering and Operations Research at Columbia University in New York. The project utilized HIVE GPUs located in Asunción, Paraguay, and the resulting research has been submitted to The Conference on Neural Information Processing Systems (NeurIPS), one of the three primary high-impact conferences in machine learning and artificial intelligence globally, held annually in December.
Intercontinental AI Training: A Proof of Concept
The research establishes a proof of concept for intercontinental AI training, with researchers based in New York City successfully executing iterative training runs on GPUs physically located in Asunción, Paraguay — more than 5,000 miles away. A key finding of the study was that, following code optimizations developed by the Columbia team, HIVE's A40 GPUs matched the performance of newer-generation H100 GPUs after normalizing for each hardware platform's raw performance.
The Columbia research team focused on neural network pretraining using optimization theory over general geometry and under large noise, designing and analyzing an accelerated algorithm that matches the performance of the current leading method, Muon, in both theory and practice. The team tested their approach on large language model (LLM) pretraining workloads of up to 1.4B parameters.
Key aspects of the research and performance benchmarking are summarized below:
| Parameter: | Details |
|---|---|
| GPU Hardware Used: | A40 GPUs (HIVE, Asunción, Paraguay) |
| Benchmark Comparison: | H100 GPUs |
| LLM Pretraining Scale: | Up to 1.4B parameters |
| Optimization Method Evaluated: | Muon and related scale-invariant variants |
| Additional Tests: | Serving throughput and latency for 1.4B model; LLaMA model throughput and latency |
| Research Submission: | NeurIPS (annual conference, held in December) |
| Collaboration Partner: | Department of IEOR, Columbia University, New York |
Foundation for Paraguay HPC/AI Gigafactory
Using the measured token-per-second, latency, and bandwidth data as a baseline, HIVE has established a foundation for an HPC/AI Gigafactory in Yguazú, Paraguay. The Company has a 100 megawatt (MW) substation under construction at the site, with civil works already complete.
The key infrastructure milestones for the Yguazú development are outlined below:
| Milestone: | Timeline |
|---|---|
| Civil Works: | Complete |
| Substation Commissioning: | Expected this summer |
| Substation Energization: | September 2026 |
| Tier-III Data Center Construction Start: | Fall 2026 |
| Ready-for-Service Date: | H2 2027 |
Leadership and Academic Commentary
Frank Holmes, Executive Chairman of HIVE, described the milestone as an important step in the Company's mission to bring advanced AI computing infrastructure to Paraguay. He highlighted that the collaboration demonstrates high-performance computing need not be limited by geography, and noted that Paraguay possesses the power, strategic location, and now a validated proof point for participation in the global AI economy.
Aydin Kilic, President and CEO of HIVE, emphasized the significance of the engineering outcome, stating that the results on HIVE's A40 nodes — after normalizing for each hardware platform's raw performance — matched those observed on H100 systems. He also referenced HIVE's broader engineering history, including building the BuzzMiner in collaboration with Intel Corporation and becoming one of the largest demand-response participants in Sweden.
An Assistant Professor in the Department of Industrial Engineering and Operations Research at Columbia University noted that the work advances understanding of modern neural network optimization, including matrix-aware optimizers such as Muon and related scale-invariant methods, and highlighted the potential relevance of these findings for future LLM pretraining.
How will the cost efficiency of training models on older-generation A40 GPUs in Paraguay compare to using newer H100 clusters in traditional US data hubs?
What specific commercial clients or research partners is HIVE targeting for the Yguazú Gigafactory once it becomes operational in H2 2027?
Could the success of intercontinental AI training from Paraguay accelerate a broader trend of decoupling AI infrastructure development from high-cost geographic regions?

























