Taipei, Taiwan – January 28, 2026 – Leading enterprise storage provider QSAN Technology Inc. has collaborated with Professor Dai Tien-Shih’s team from the Department of Information Management and Finance (IMAF) at National Yang Ming Chiao Tung University (NYCU) to deploy an innovative dual-tier storage architecture. This solution addresses the high-concurrency demands of generative AI workloads, earning exclusive coverage in Digitimes and positioning QSAN as a frontrunner in AI financial infrastructure.
Digitimes Spotlights Industry-Academia Breakthrough
Digitimes featured the partnership in its in-depth report: “QSAN Teams with NYCU to Build Replicable Dual-Tier Storage for AI Financial Analysis”. QSAN’s enterprise AI decision analytics solutions evolve from campus proofs-of-concept to scalable platforms for quantitative trading, fintech, and big data analytics—redefining data architecture for the AI era.
High-Concurrency Era Demands Storage Evolution
As generative AI and quantitative analytics mature, financial research has shifted from single-model validation to multi-model parallelism and high-frequency backtesting. Exploding AI model sizes and financial datasets elevate storage from a support layer to a core determinant of scalable AI deployment.
Industry-wide, AI applications now require baseline high-concurrency computing. Quantitative trading, fintech, and enterprise AI analytics demand simultaneous high-throughput access, multi-model processing, and multi-user operations—exposing limitations in legacy monolithic storage.
NYCU IMAF encountered these challenges firsthand in AI stock analytics and strategy backtesting, where single-tier storage bottlenecked multi-strategy parallelism. Partnering with QSAN, Professor Dai’s team redesigned the platform around workload-tiered dual storage, enabling seamless parallel operations.
Dual-Tier Architecture: Optimized for Latency and Throughput
For high-frequency trading (HFT) and tick-level analytics, the architecture delivers sub-millisecond latency and massive IOPS. Low-latency strategy backtesting and AI inference run on the QSAN XF Series NVMe all-flash array; research datasets, models, and outputs reside on the QSAN XN Series unified storage, supporting concurrent multi-user access.
Scalable Beyond Academia: An Industry Blueprint
This model extends beyond education/research environments to quantitative trading, financial services, analytics teams, and enterprise AI deployments—balancing performance, reliability, and TCO.
Professor Dai’s deep expertise in AI finance provided rigorous theoretical and practical foundations, refining the architecture for precision. This academia-industry synergy boosts compute efficiency and responsiveness, setting a benchmark for future collaborations.
Storage as Core AI Financial Edge
“AI models are now central to financial research and validation,” notes Professor Dai Tien-Shih. “Underlying storage dictates research depth and responsiveness. Platforms enabling concurrent high-performance computing and multi-user collaboration are essential for scaling intelligent finance and AI applications.”
Campus as Vanguard for Enterprise Adoption
Generative AI’s model proliferation and mounting financial data volumes strain analytics platforms. Academic labs pioneer architectures like QSAN’s dual-tier design, offering enterprises proven blueprints for AI infrastructure.
