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[Runyour AI Interview] Expanding the Boundaries of Experimentation with Runyour AI
2026.02.04.
"Will Our Environment Handle a Bigger Model?"—The Real-World Dilemma of a Student Team
In an era where large language models have become the baseline, every student team running an AI project tends to face the same practical question:
"There's so much we want to try—but will our environment actually run it?"
Team "AI-deul" from Inha University, winners of both the Grand Prize (Minister of Science and ICT Award) and the Popularity Award at the 2025 Digital Competition, started from exactly this dilemma. Their challenge was to build a model that could distinguish AI-generated text from human-written text—a problem that, by its very nature, all but required leveraging large language models. We spoke with the team about how they expanded the scope of their experiments within a constrained research environment and ultimately delivered a top-tier result.
A Conversation with Team "AI-deul" from Inha University
2025 SW Convergence College Digital Competition Grand Prize – Team "AI-deul," Inha University
Q. What was the most pressing practical constraint you faced during the project?
By far, the biggest constraint was GPU resources. As models grow larger, both compute requirements and memory needs scale dramatically—and when GPU VRAM falls short, certain experiments simply aren't possible to begin with.
The other major issue was continuity of experimentation. When training runs get interrupted halfway, or when too much time is spent just configuring the environment, you end up pouring far more energy into maintaining the environment than into actually improving model performance. These constraints go beyond mere inconvenience. They can wear down a team mentally, and more critically, they end up defining the very range of models you're able to try.
Q. How did you use Runyour AI in the project, and what changed as a result?
Runyour AI served as the core infrastructure for model training and experimentation throughout the project. In particular, having access to a high-spec GPU environment with 80GB of VRAM allowed us to actually load and train large LLMs that we previously couldn't even attempt. That opened up real room to compare different model architectures, analyze how performance varies across parameter scales, and run the iterative training cycles needed for fine-grained tuning.
The biggest shift we felt was that the baseline of what we considered "an experiment" had completely changed. The headroom that comes with ample VRAM made a real difference, and just being able to choose models without worrying about memory limits gave us far more freedom in how we approached the project. Training times shortened, our experimental turnover rate went up, and minimizing setup time meant we could put our focus squarely on model architecture and performance gains.
On the cost side, a pay-as-you-go pricing model felt genuinely reasonable from a student team's perspective. Experiments that would have been completely out of reach under a long-term fixed-cost model suddenly became realistic options.
Q. What kind of impact did Runyour AI have on your final result?
I'd say Runyour AI wasn't just a convenient tool—it was much closer to a prerequisite that made the Grand Prize result possible. Because we could fully experiment with large models, our range of model choices widened, and that translated directly into consistently strong rankings on the final leaderboard.
Ultimately, this kind of competition comes down to how many experiments you can run, and how deeply you can run them. The Grand Prize was the cumulative result of the difference our experimental environment made.
Infrastructure Defines the Limits of Experimentation
Inha University's Team AI-deul shows that in AI projects, infrastructure isn't a supporting role—it's a core factor that defines how far experimentation can go. Runyour AI gave these students the environment to attempt larger models, and that choice ultimately translated into a Grand Prize result.
Whenever large-scale compute is essential—across research, education, competitions, and real-world projects—Runyour AI stands alongside its users as the GPU infrastructure that lets them focus on ideas and experimentation, not environment setup.
