When Quantum Algorithms Meet GIGABYTE AI TOP 100

GIGABYTE AI TOP 100 Product Deployed

GPU: NVIDIA RTX 5090
OS: Windows Native
Deployment: On-Premise / Air-Gapped
Dev Stack: Python / VS Code

Powered by the NVIDIA RTX 5090, the AI TOP 100 natively supports Windows-based Python/VS Code workflows. It delivers the full compute capacity needed for on-premise quantum algorithm inference, GAN skin image model training, and multi-layer image analysis — enabling a complete AI R&D pipeline with zero cloud dependency.

Key Metrics

AI TOP 100 + RTX 5090
37hrs

to complete quantum-scale compute tasks that previously took over a year

Quantum-scale compute: 1 year → 37 hours
AI TOP 100 + RTX 5090
10min

to screen 100K+ drug candidate molecules

High-throughput screening of 100K+ drug candidates
AI TOP 100 + RTX 5090
Zero

cloud dependency — fully air-gapped on-premise deployment

Cloud dependency — fully air-gapped on-premise

The Challenge: Compute Ceiling and Data Security Dilemma

Yuan An Biotech’s DFT quantum algorithm demands an enormous number of iterations. On conventional CPU servers, a single computation cycle could easily take over a year, leaving R&D perpetually bottlenecked by compute constraints. But the harder problem was data security: their proprietary algorithm, already published in Nature Scientific Reports, along with client-commissioned biotech formulations, represented IP that could never be placed on any cloud platform. SaaS and public cloud solutions were off the table from the start.

Compute Demands Exceed CPU Limits

Compute Demands Exceed CPU Limits

The complexity of quantum algorithms far exceeds conventional workstation capacity. The team previously relied on the NCHC, leaving R&D timelines dependent on external resource availability.
Cloud Deployment Is Not an Option

Cloud Deployment Is Not an Option

Core algorithms and client formulations represent highly sensitive IP. Both regulatory requirements and commercial obligations demand full physical isolation.
High Deployment Barrier Excludes SMEs

High Deployment Barrier Excludes SMEs

Traditional AI servers require Linux environments, creating operational barriers that most small and mid-sized biotech companies cannot easily overcome.
IRB Clinical Trial Costs Are Prohibitive

IRB Clinical Trial Costs Are Prohibitive

A single-product human trial costs NTD 4 to 5 million. With the PIF regulation mandate approaching, alternatives are urgently needed.

The Solution

The GIGABYTE AI TOP 100, powered by the NVIDIA RTX 5090, natively supports the Python/VS Code workflows that the research team already uses, running on Windows straight out of the box. Researchers don’t need to configure complex server environments or wait for external resources. They simply power on the AI TOP 100 and start running models. Fully on-premise, fully self-contained — for many small and mid-sized biotech companies, this is exactly the tool they’ve been waiting for.

The performance gap is most striking when put in concrete numbers. A conventional CPU server running 100 iterations of a DFT quantum algorithm would take well over a year. On the AI TOP 100 with its RTX 5090, a task ten times larger — 1,000 iterations — was completed in just 37 hours. This is not about doing the same thing faster; it’s about doing something harder in a fraction of the time. When a single validation cycle goes from “wait a year” to “wait a weekend,” the entire R&D cadence is fundamentally rewritten. Research directions previously shelved due to prohibitive compute costs and timelines now have a real chance of being revisited.

Conventional CPU Server

1⁺Years

to complete quantum algorithm computation

AI TOP 100 + RTX 5090

37Hours

to complete the equivalent task

＊This is not about doing the same thing faster; it’s about doing something harder in a fraction of the time.

Within Yuan An Biotech’s R&D workflow, the AI TOP 100 plays twocriticalroles. Before lab development begins, AI performs aninitialscreening — rapidly scoring and ranking hundreds of thousands of candidate formulations or molecular structures, filtering out low-potentialoptionsearly, concentrating R&D resources on the most promising directions. Once a direction is confirmed, AI shifts to a formulation optimization role, simulating how different ingredient combinations perform on humanskinand progressively narrowing the optimal solution space. The combined result: fewer physical trials, shorter development cycles, and more confident formulation decisions.

In the skincare development space, Yuan An Biotech faces a very real market challenge. With PIF regulations taking effect in July 2026, every efficacy-claim skincare product must be backed by full scientific documentation. Traditional IRB human trials cost NTD 4 to 5 million per product — a threshold that effectively bars most small and mid-sized brands. The AI TOP 100 plays a genuinely meaningful role here: through AI simulation and skin image analysis, brands can now build efficacy validation reports within a compliant framework at a fraction of historical costs. This is notmerely cost savings — it isusingscientific evidenceto replace IRB testingIRB — for the first time truly accessible to small and mid-sized biotech companies.TheThe AI TOP 100 supports the full pipeline from data preprocessing through model training to image comparison, trained on 500,000 skin images spanning ages 18 to 65.

▲ Skin image matrix trained on the AI TOP 100, covering skin condition prediction for Asian males and females aged 18 to 65

AI TOP 100 in Action: GAN Model Training Progression

Training the skin image model was a process requiring both time and sustained compute. On the AI TOP 100, the journey progressed through four key stages.

First: Large-Scale Data Visualization: 500,000 skin images from Asian males and females aged 18 to 65 were structured and organized to establish a baseline portrait of skin conditions across age groups.

Second: Model Training: The AI repeatedly processed the real skin samples, refining its interpretation criteria with each iteration, progressively learning to distinguish subtle differences across age groups and skin conditions.

Third: Establishing Predictive Benchmarks: Once training converged, the model became capable of predicting the degree of skin condition improvement achievable with a given formulation, producing efficacy benchmarks that brands can directly reference.

Fourth: Comparison and Simulation: Using these benchmarks, multi-layer comparisons were conducted on before-and-after skin images to quantitatively verify real-world formulation efficacy.

The AI TOP 100 demonstrated its value throughout the training process: at Epoch 3, model output was still a vague outline; by Epoch 100, skin texture and layering began to emerge; after completing 1,000 epochs of deep iteration, the generated images closely replicated real skin detail, reaching a precision level suitable for efficacy evaluation.

（*An epoch is one complete pass through the entire training dataset. The higher the epoch count, the more learning cycles the model has completed, and the higher its interpretive accuracy.）

AI TOP 100 in Action: Multi-Layer Image Analysis

After model training, the AI TOP 100 supported multi-layer image comparison analysis, translating AI interpretations into efficacy data that brands can directly cite. The system accurately detects differences between two sets of skin images — not only visible texture changes, but also brightness distribution and cellular alignment — and automatically identifies regions with the most significant differences, providing objective, quantifiable evidence for efficacy comparisons.

The system also features image alignment correction, automatically ensuring comparisons are made at the same skin location, eliminating errors from shooting angle variations (feature point alignment: 12 high-confidence matches from 151 candidate points). A deep learning model performs semantic-level similarity analysis, capturing skin condition changes invisible to pixel-based comparison, achieving a similarity score of 0.7688 (VGG16 deep feature comparison).All computation and analysis runslocally on the AI TOP 100 — core data never leaves Yuan An Biotech’s lab.

AI TOP Utility — Intelligent Oversight Beyond the Hardware

Beyond raw compute, GIGABYTE AI TOP Utility is the layer that keeps the entire R&D workflow running smoothly. Yuan An Biotech’s technical team said one of the things they appreciate most is the real-time status display on the Dashboard — no need to open a terminal, no command lines. At a glance, they can see GPU utilization, available memory, and task progress. This is exactly what AI TOP Utility is designed for: letting researchers focus on their research, not on managing their tools.

“The GIGABYTE AI TOP 100 means we no longer need to queue for the national computing center, and we no longer worry about our core formulations being exposed. One workstation-class device solved everything we used to think required an entire server room.”
— Yuan An Biotech, Technology Team

Real-Time Hardware Dashboard

Real-Time Hardware Dashboard

GPU/CPU load, memory usage, and SSD status visible at a glance. Monitor compute consumption in real time during GAN training and drug screening tasks — no command line required.
In-App Model Validation

In-app Model Validation

Validate model performance directly within Utility immediately after training. Rapid iteration and optimizationb with no separate test environment needed.
Dataset Creation and Management

Dataset Creating Tool

Create and manage custom training datasets with optimization tools to improve skin image model accuracy, all within the Windows environment.
Seamless Windows WSL Integration

Windows Subsystem for Linux

Run AI TOP Utility natively on Windows via WSL. Zero learning curve for researchers — no extra setup, no extra effort.

Key Outcomes

Compute
1 Year → 37 Hours

The RTX 5090 dramatically compresses quantum algorithm compute time, directly accelerating R&D iteration speed.
Deployment
Zero IT Barrier

Native Python/VS Code support allows researchers to self-manage the system — no dedicated IT staff required.。
Security
Air-Gapped IP Protection

Algorithm and formulation data never leaves the local device, meeting the highest biotech data security standards.
Compliance
Replaces IRB Trials

AI efficacy validation reports comply with PIF regulations, replacing clinical trials that cost NTD 4 to 5 million per product.

Why Choose AI TOP 100

At the close of the interview, Yuan An Biotech offered one line — simple, but carrying real weight: “Everyone deserves access to AI.” This is not a slogan. It’s a genuine conviction, distilled from the firsthand experience of a company working on the front lines of biotech R&D. Precision AI compute has historically been the exclusive domain of major pharmaceutical companies and multinational conglomerates — a barrier that placed it out of reach for most small and mid-sized biotech firms. The GIGABYTE AI TOP 100 rewrites that reality. No massive IT infrastructure required. No cloud subscription. One AI TOP 100 unit, one Utility interface, and R&D teams can now command the kind of compute power that was once reserved for large institutions. For Yuan An Biotech, choosing the AI TOP 100 was not merely a procurement decision — it was a statement: small and mid-sized biotech companies in Taiwan are fully entitled to take the stage in global precision medicine.

These results were also made possible by the end-to-end support of GIGABYTE’s authorized reseller partner, Tatung World Technology (tsti). With deep expertise in ICT system integration, tsti guided the team through product selection, system configuration, and deployment, helping the R&D team navigate every technical challenge along the way and ensuring every unit of compute was precisely matched to real-world needs — serving as the indispensable technology bridge between Yuan An Biotech and the AI TOP 100.

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When Quantum Algorithms Meet GIGABYTE AI TOP 100

GIGABYTE AI TOP 100 Product Deployed

Key Metrics

The Challenge: Compute Ceiling and Data Security Dilemma

Compute Demands Exceed CPU Limits

Cloud Deployment Is Not an Option

High Deployment Barrier Excludes SMEs

IRB Clinical Trial Costs Are Prohibitive

The Solution

AI TOP 100 in Action: GAN Model Training Progression

AI TOP 100 in Action: Multi-Layer Image Analysis

AI TOP Utility — Intelligent Oversight Beyond the Hardware

Real-Time Hardware Dashboard

In-App Model Validation

Dataset Creation and Management

Seamless Windows WSL Integration

Key Outcomes

1 Year → 37 Hours

Zero IT Barrier

Air-Gapped IP Protection

Replaces IRB Trials

Why Choose AI TOP 100