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Scaling Quantum-Inspired LLM Fine-Tuning Research with H200 GPUs | Korea University AI and Mobility Lab
2025.10.16.
The AI and Mobility Lab at Korea University's School of Electrical Engineering focuses its research on reinforcement learning, large language models, mobility control, hallucination detection algorithms, and quantum machine learning. The lab explores autonomous control and cooperative decision-making across a wide range of mobility platforms—including UAVs, UAMs, AUVs, and satellite networks—while also pursuing next-generation computational paradigms such as quantum reinforcement learning and quantum federated learning.
At the core of this Runyour AI use case is the optimization of LLM fine-tuning. To address the parameter and memory demands of existing approaches—Full Fine-Tuning, LoRA, and Prefix Tuning—the team designed QAA (Quantum-Amplitude embedded Adaptation), a quantum-inspired framework, and ran experiments integrating it directly into real LLM architectures.
The target models included GPT-Neo, GPT-J, and the LLaMA family. Using instruction datasets such as Alpaca, the team conducted quantitative comparisons against LoRA, SoRA, and Prefix Tuning, evaluating performance across natural language generation metrics (BLEU, ROUGE, BERTScore) and selected GLUE tasks. Throughout this process, the team relied on two H200 GPUs from Runyour AI to secure the compute power and memory headroom required for large-scale model experimentation.
■ Large-Scale LLM Fine-Tuning Made Possible by H200 GPUs
In LLM research, the compute and memory demands of experiments scale dramatically as both model size and dataset size grow. This is especially true for research like QAA, where new modules are integrated into the LLM backbone—work that requires not just basic model execution, but a GPU environment capable of stably supporting a wide range of structural variants and iterative training.
Runyour AI gave the team on-demand access to H200-class GPUs whenever they were needed. This allowed the team to actually run hybrid training pipelines that combine quantum circuit modules with LLM backbones—without having to aggressively reduce batch sizes, sequence lengths, or attention head scale.
The benefit of high-performance GPU resources went well beyond simply speeding up training. By maintaining larger experimental configurations, the team was able to validate the effects of the QAA module under more realistic conditions and make more precise performance comparisons against existing fine-tuning techniques.
■ A Research Pipeline from Data Preprocessing to Validation
The team's experimental pipeline followed a clear flow: data preprocessing → quantum embedding → fine-tuning → validation and analysis. In the quantum embedding stage, the team built a module that maps input features through amplitude encoding, enabling a structure that increases expressiveness with fewer parameter changes.
During the training stage, the team experimented with various architectural configurations—separating and combining the QAA module with the LLM backbone in different ways. They examined insertions before and after attention blocks, integration via residual connections, and parallel use with low-rank approximation methods to test how broadly QAA could be applied.
In the validation stage, the team evaluated not only natural language generation metrics, but also training curve stability, memory footprint, and throughput per step. Runyour AI's stable sessions and consistent resource environment made it easy to rerun experiments under identical conditions and average results across multiple runs—enabling rapid iteration on architectural ablation studies.
■ Immediacy and Parallelism That Accelerated Research
When choosing Runyour AI, two factors weighed heavily for the team: immediate access to the latest GPU class, and the stability to support continuous experimentation. LLM research is compute-intensive, and when high-performance GPUs aren't available at the moment they're needed, schedules slip and teams are often forced to compromise on model size or batch configuration.
Runyour AI offered a flexible environment where experiments could begin the moment a specification was selected. With no waiting and no complex reservation process, the team was able to run continuous experiments and parameter sweeps—spending far more time on model architecture and algorithm validation than on resource provisioning.
The ability to run multiple experiments in parallel was another major advantage. The team could carry out hyperparameter sweeps and multi-seed validation within tight timelines, keeping experiment queues short and accelerating the design–validation loop.
Korea University's AI and Mobility Lab demonstrates how a high-performance GPU cloud can expand both the speed and the scope of LLM research. Backed by H200 GPUs, the team carried out QAA-based LLM fine-tuning experiments and was able to validate an entirely new research direction—one that combines quantum-inspired frameworks with large language models.
Runyour AI did more than supply GPU resources. It gave the researchers an environment in which the burden of resource provisioning and system operations was minimized, freeing them to focus on experimental design and the validation of new ideas. In LLM research, where long training runs and iterative experimentation are central, a stable high-performance GPU environment translates directly into research productivity.
