Corporate AI Training

No More AI Training That Doesn’t Translate to Real Work

Elice designs training that reflects the latest AI trends, builds the AI capability your organization needs, and drives AX.

Cumulative clients

Cumulative users

ax_education.stat.patents.suffix

Tech patents

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Papers in intl. journals

AI training that stops at tool tutorials isn't enough

Training that does not translate into real work

“We learn how to use the AI tools, but applying them to our actual work is difficult.”

One program with no audience-level design

“Our audiences are different, but there is only one program.”

Training that cannot be applied in secure environments

“It centers on external AI tools, so it cannot be applied as-is on a closed network.”

One-off sessions with no follow-through

“The training ended, but the organization’s AX has not moved.”

AX Environment Connecting Training, Platform, and Infrastructure

Practice Environment Isolated from the Outside

Even ChatGPT and Claude, hard to use on a company network, run inside an isolated virtual container environment that blocks data from leaving.

AI Development Environment Identical to Actual Work

In the RunBox virtual environment on Elice LXP, use hands-on AI tools such as VS Code, n8n, and Jupyter together with Elice Cloud GPU resources, with no setup required.

Enterprise AX Agents

Research and document work run on the conversational Helpy Chat; development runs on Helpy Code, a coding agent that keeps your data in house.

Customized AX Training Roadmap Tailored to Organizational Goals

If You Want to Solve Problems Based on Your Organization’s Data

Solving problems with your own company data is what lifts learning impact and on-the-job adoption the most.

Guided Project & PBL Using Internal Data

AI Solution-Linked Training

AI Reskilling Training

If You Want to Solve Problems Based on Your Organization’s Data

If You’re Planning or Expanding an Organization-wide AX Roadmap

Design a training system for each position, rank, and department, and gradually expand an organization-wide AX roadmap.

Tailored Training by Position and Role

AX Literacy Training

AI Solution-Linked Training

If You’re Planning or Expanding an Organization-wide AX Roadmap

If You Want to Quickly Apply the Latest AI Trends to Real Work

From generative AI to document automation and literacy, your organization can quickly equip itself with the most up-to-date practical skills.

AX Literacy Training

Tailored Training by Position and Role

LLM · AI Agent Training

If You Want to Quickly Apply the Latest AI Trends to Real Work

*PBL (Problem Based Learning): learning by defining and solving real business problems firsthand.

Tailored training for every level, from executives to key talent

We have designed and run level-specific training for everyone from executives to practitioners at Hyundai Motor Group, Samsung Electronics, LG Electronics, SK, Kia, and other leading Korean enterprises.

Executive program

Covers how AI reshapes industries and business strategy, and the principles to weigh before adopting it.

Read the K-Hitech Platform story Read the Hyundai Motor Group story

Team lead program

Learn how to apply AI to team workflows and drive adoption across members.

Company-wide program

Builds the fundamentals so every employee understands generative AI and applies it to documents and task automation.

Read the SPC Group story

Key talent program

Develops in-house builders through bootcamps, hackathons, and other formats where they create AI services themselves.

Read the FuriosaAI story

Six training programs designed around your organization

The same course produces different results when the hands-on work runs on your own company data. We redesign every program around your goals and your data.

Tailored AX Training Management for Organizations

Needs Analysis

We thoroughly diagnose your organization's AI capability and training needs.

Curriculum Design

We configure custom curricula that can be put to use immediately in the workplace.

Lab Environment Setup and Operation

We build and operate dedicated LXP environments for training.

Adoption at Work

We assist with real-world application and organizational expansion based on training outcomes.

AX that doesn't stop at training

From identifying AI initiatives and developing custom models to deploying agents, we cover everything it takes to become a true AX organization.

Identify AI initiatives
We start by defining the problems worth solving with AI, based on field interviews and a data diagnosis.
Model development
We design and build custom models that fit your in-house data and infrastructure.
Model enablement training
We train your own team to operate and improve the model without outside help.
Agent deployment
We connect agents to real workflows so the training turns into measurable results.

Identify AI initiatives
We start by defining the problems worth solving with AI, based on field interviews and a data diagnosis.
Model development
We design and build custom models that fit your in-house data and infrastructure.
Model enablement training
We train your own team to operate and improve the model without outside help.
Agent deployment
We connect agents to real workflows so the training turns into measurable results.

Explore Corporate AI Transformation

Proven Training Partner, Elice

Hyundai Motor Company Namyang R&D Center: Building a 99%-Accurate Image Search and Classification System for Crash Testing

Finding a Way to Use an Overabundance of DataHyundai Motor Company’s Namyang R&D Center faced growing challenges in managing and utilizing the massive volume of image data generated during vehicle crash safety tests. Each single crash test produces high-speed camera footage, tens of thousands of high-resolution images, and measurement data recorded by thousands of sensors. However, image data was stored across multiple folders without a consistent classification standard.To find deformation cases for a specific vehicle model and viewing angle, researchers often had to ask colleagues who had conducted the test or manually open countless folders and check thumbnails one by one. Preparing a single report required an average of 20–30 minutes just to locate and organize relevant images, creating a clear bottleneck in research efficiency and data reuse.“We had a lot of data, but no efficient way to process it. To find images of a specific vehicle and angle, we had to ask colleagues or manually check thumbnails one by one.” — Researcher, Hyundai Motor Company Namyang R&D CenterTo overcome these limitations and improve research efficiency, Hyundai Motor Company’s Namyang R&D Center decided to build an AI-based image classification and search system. Elice was selected as the technology partner to develop a solution optimized for the crash test environment. An AI Search Engine That Finds Photos Using PhotosIn crash test analysis, researchers need to quickly compare images from similar cases. When this process relies on human memory and manual searching, data utilization inevitably becomes inefficient—no matter how much data is accumulated. What researchers needed was the ability to search for images using images: an environment where images matching specific conditions could be instantly retrieved and compared.The challenge was that conventional image search methods were not suitable for the crash test context. For example, when submitting a diagonal-view image of a vehicle, results often prioritized images with similar colors or external appearances rather than the same viewing angle, making it difficult to find analytically relevant cases. To address this, Elice designed a search system that focuses not on visual similarity but on camera angle (view) and composition.“Conventional general-purpose models are trained mainly on visual features such as shape and color, so they struggle to classify images based on camera angle, which is critical in crash testing. Hyundai needed a model where a diagonal-view image of a white SUV would return diagonal-view images—even if they were of a red compact car.” — Suin Kim, CRO (Chief Research Officer), EliceAs a result, researchers can now explore similar cases based on meaningful analytical criteria—camera angle—without being affected by vehicle type or color. Precision Search Down to the Finest DetailsIn crash test analysis, not only the overall vehicle shape but also subtle changes—such as bumper curvature or damage to a lamp—often provide critical insights. Elice implemented a focus search feature that allows users to select a specific region of interest within an image, assigning greater weight to that area when retrieving similar images.This feature captures both the global context and fine-grained details of an image, enabling highly precise searches—like finding a needle in a haystack within a massive archive. Researchers can now selectively retrieve deformation cases of specific components at specific angles. High Accuracy and Stability for Real-World UseElice began the project by deeply understanding how Hyundai’s researchers actually work—how crash test data is generated, stored, searched, and used in practice. Based on this analysis, Elice designed a custom labeling framework that accounted for camera angles, vehicle parts, and test conditions. The goal was not simply to build a technically feasible system, but one that researchers could genuinely use in their daily work.For Namyang R&D Center, the objective was not an experimental model, but an automation system deployable in real operations. Model accuracy was therefore a critical requirement. If accuracy were insufficient, human revalidation would still be required, limiting the benefits of automation.With data reliability as the top priority, the Elice AI team set a high accuracy target suitable for real-world use. As a result, the image classification system achieved an accuracy level of approximately 99%.“We initially thought around 95% accuracy would be sufficient, but Elice proposed targeting over 99% from the beginning.” — Researcher, Hyundai Motor Company Namyang R&D CenterBeyond technical development, Elice also took on the role of an AI project manager (AI PM) to ensure the project’s successful execution. In large-scale AI projects, effective communication with stakeholders, schedule management, priority setting, and strategic alignment are just as important as model performance. Managing these elements systematically is essential for delivering a solution that can be applied in real-world operations.“In large-scale AI projects like this, the AI PM role—overseeing development, communication, and scheduling—is critically important. We systematically managed everything from communication with practitioners to modeling decisions, direction alignment, and priority management, enhancing both the completeness and stability of the project.” — Suin Kim, CRO (Chief Research Officer), EliceDeveloping High-Precision AI Models Through Large-Scale TrainingOnce the labeling framework was established, Elice conducted large-scale training using its high-performance computing infrastructure. Through tens of thousands of iterative training cycles, the team continuously improved model accuracy and applied data augmentation techniques to ensure consistent performance across diverse shooting conditions and vehicle types.The model was ultimately advanced beyond simple visual similarity matching to a level where it could understand spatial context and measure semantic similarity.“Elice researched multiple approaches with us and significantly refined the system. We were very satisfied with the results.” — Researcher, Hyundai Motor Company Namyang R&D CenterSecure and Stable System Integration and DeploymentAfter model development was completed, the system was integrated with Hyundai Motor Company’s existing research infrastructure. Because the system handles highly sensitive research data, security and stability were non-negotiable prerequisites.Elice strictly adhered to Hyundai’s security guidelines when designing the enterprise cloud environment and built an intuitive interface that researchers could easily use. Even after deployment, Elice continuously collected and reflected user feedback to further improve usability in real-world research environments.From Images to Documents: A Scalable Multi-Data Understanding Architecture Beyond building an image-based search and classification system, Elice also considered a technical architecture scalable to broader research data environments. In real-world research settings, not only images but also Excel files, PowerPoint documents, and handwritten materials serve as critical analytical assets.Elice has developed 'Helpy Vision', a model equipped with proprietary Document Parser technology that automatically recognizes document layout elements and converts tables, formulas, and charts into structured data. Helpy Vision understands different document components—such as paragraphs, tables, figures, and formulas—in reading order and transforms them into structured data that AI systems can utilize.Built as a Vision-Language Model (VLM), Helpy Vision jointly interprets images and documents, enabling integrated data understanding that goes beyond image search to include document context. This capability is designed to expand toward Vision-Language-Action (VLA) models in the future, enabling applications in physical AI environments.Advancing AX with EliceElice supports AI system development end to end—from data organization and model development to real-world deployment—while ensuring not only accuracy but also operational stability and practical applicability. Even in highly specialized industrial data environments such as crash testing, data only becomes an asset when it can be effectively used.If you are looking to build an AI environment that allows your organization to leverage large-scale data more quickly and accurately, start with Elice.

2/23/2026

Soombit AI Trains on 14 Million Medical Images, Pursuing Korea’s First Regulatory Approval for a Generative AI Medical Device

Training vast amounts of medical data for AI models requires both high-level security and cost efficiency. Healthcare AI startup Soombit AI trained on 14 million medical images using Elice Cloud, securing both safety and cost efficiency while pursuing Korea’s first regulatory approval for a generative medical device.Recently, Soombit AI, a healthcare AI startup aiming for Korea’s first regulatory approval of a generative medical device, has been drawing attention. The chest X-ray draft report generation software currently under development by Soombit AI has received approval for its clinical trial plan (IND) from the Ministry of Food and Drug Safety and has entered the clinical stage. The era in which generative AI drafts radiology reports in real clinical settings is fast approaching.Globally, demand for medical imaging exams continues to rise. As screening and early diagnosis become more emphasized, the number of imaging studies such as X-rays, CT scans, and MRIs is rapidly increasing. However, the number of radiologists available to interpret these images is not growing at the same pace. As a result, delays in interpretation, increased workload for medical staff, and delays in care for emergency and critical patients are recurring issues in healthcare settings. Soombit AI is building infrastructure to alleviate these structural bottlenecks with generative AI technology, helping radiologists complete interpretations more quickly and reliably. Soombit AI’s Vision-Language (VLM)-Based Diagnostic AI ModelSoombit AI is a healthcare AI startup developing a vision-language-based generative AI model that understands both medical images and related clinical information. Its chest X-ray–focused software, “AIRead-CXR,” automatically generates draft radiology reports based on chest X-ray images. It analyzes a variety of findings that need to be detected in the images and presents personalized draft reports along with information on potential abnormalities, helping radiologists complete interpretations more quickly and consistently. Radiologists no longer need to write reports from scratch. Instead, they can finalize interpretations by reviewing and editing drafts generated by AIRead-CXR. This approach aims to reduce repetitive documentation tasks and allow physicians to focus more on image analysis and clinical judgment. Infrastructure Requirements for Safely Training Healthcare DataThe data handled by Soombit AI consists of sensitive healthcare information. Therefore, when selecting a cloud infrastructure, the company’s top priority was not performance or convenience, but data security and regulatory compliance. Because Soombit AI collaborates with domestic medical institutions, it was crucial to confirm whether data centers were physically located in Korea and how storage and computing resources were separated and managed.“As a company dealing with medical data, the data center absolutely had to be located in Korea. At the same time, we had to consider GPU specifications, speed, and pricing. There were not many options that could meet all of these requirements at once.” — Jawook Gu, AI Research Director, Soombit AISolving Security and Cost at the Same Time with Elice CloudSoombit AI chose bare metal servers on Elice Cloud. Training generative models requires large computational resources for each training run, and repeated experimentation is essential to improve model performance. In usage-based pricing structures, costs accumulate every time an experiment is added, which can create psychological and financial pressure when researchers try to expand their experiments. In contrast, bare metal servers provide dedicated resources for a set period, allowing research to proceed by running as many experiments as possible without interruption.“Each training run required substantial computational resources, and we had to run many experiments. In an on-demand environment, adding experiments would continuously increase costs. Thanks to Elice Cloud’s recommendation of bare metal, we were able to expand our experiments with confidence and begin training without hesitation.” — Jawook Gu, AI Research Director, Soombit AIIn addition, bare metal servers allow for completely separated storage structures, which is advantageous from a healthcare data security perspective. With servers located in Korea and storage and computing resources managed separately, Soombit AI was able to build an infrastructure that met regulatory and security requirements more easily. Training on 14 Million Images and Moving Toward Korea’s First Regulatory ApprovalUsing Elice Cloud, Soombit AI trained a generative medical AI model on 14 million chest X-ray images. Generative models are highly sensitive to configuration and data composition. To improve performance, numerous combinations must be tested through repeated training and validation. Thanks to the bare metal environment, Soombit AI was able to expand its research more aggressively and steadily improve model performance.“While using Elice Cloud, we were able to run a large number of experiments without burden, and through that process, we improved model performance step by step. As a result, we were able to publish more than four papers at international conferences over the past year.” — Jawook Gu, AI Research Director ▲Soombit AI Published PapersWith sufficient infrastructure and cost efficiency to support extensive experimentation, Soombit AI achieved tangible results not only in academic publications but also in preparing for regulatory approval of a generative medical device. To develop a model suitable for real clinical environments, stability must be verified across diverse patient groups and scenarios. Based on large-scale experiments and training conducted on Elice Cloud, Soombit AI has built the technical foundation to pursue Korea’s first regulatory approval for a generative medical device. The Next Step Toward Scale-UpSoombit AI’s goal is to build a multimodal foundation model that encompasses not only the chest X-ray generative AI model currently under development but also various imaging modalities such as CT and MRI, along with clinical data.Achieving this requires a cloud infrastructure capable of greater scale and flexibility. As models grow larger and expand into multi-task learning, GPU memory and computational performance become increasingly important, along with storage capacity and speed, and network bandwidth between GPUs and storage. During intensive training phases, high-performance GPU resources must be used heavily, while during idle periods, data archiving capabilities are needed to reduce costs. Stable API communication, network environments, and strong security for service operations are also essential.Elice Cloud ECI is an IaaS platform equipped with these capabilities. It is a container-based cloud infrastructure designed to enable AI companies to carry out the entire process from research to commercial deployment on a single platform. Built on Kubernetes, it allows developers to operate model training and inference services simultaneously in familiar container environments. GPU resources can be dynamically allocated and released as needed, enabling cost-efficient management depending on whether the system is in a training or inference phase.Especially in fields like healthcare AI, where data security and regulatory compliance are critical, being able to leverage container flexibility on top of a stable, Korea-based data center infrastructure certified under CSAP is a major advantage. An Optimal Cloud Partner for Healthcare AI Companies“We believe it is the most reasonable cloud service operating servers in Korea. Because the data center is domestic, we feel secure about healthcare data protection. With a cost structure suitable for early-stage startups and flexible proposals, we were able to conduct more experiments without burden. That ultimately led to better models and faster results.” — Jawook Gu, AI Research Director, Soombit AIElice Cloud will continue to provide secure, Korea-based infrastructure and efficient AI cloud environments to help healthcare AI companies like Soombit AI develop larger-scale models and services. From early research experiments to commercial service deployment, Elice Cloud aims to build the foundation that allows healthcare AI companies to focus on their technology and products.

1/27/2026

An app that allows students and teachers to learn coding that they both find satisfactory

Busan Education Research & Information InstituteGradually, students are expected to have a basic understanding of coding, as evidenced by the increase of public education information classes and the introduction of required coding education. With the announced adoption of AI digital textbooks starting in 2025, the digital transformation of public education is anticipated to pick up speed. The Busan Education Research & Information Institute similarly felt that education needed to change to keep up with the changing fashion.An internet platform designed for efficient information instruction in classrooms was the first thing I required. An online platform that allowed for simultaneous practice was crucial because it was impossible to apply information subjects in practice alone through theory study. One issue that arose in the situation when COVID-19 compelled remote learning was that each school's approach and quality of instruction varied. However, we lacked the right online platform to address these issues. In order to fulfill the following requirements, Busan Education Research & Information Institute needs a platform.A tool that lets teachers practice coding and apply it practically in the classroomA platform with LMS administration features that facilitate effective student learning and progress tracking in condensed class periods.![엘리스LXP, 코딩실습플랫폼, LMS, 부산에듀원, 초등코딩교육](https://cdn-cms.elice.io/elice-strapi/unamed_4c800a0997.webp](https://cdn-api.elice.io/api-attachment/attachment/95e2693714374c5b88a3b22e40667faf/12_2_.webp) ▲ Why Elice's platform and content were selected by the Busan Education Research & Information InstituteElice LXP: a platform for practical application ▲ Elice LXP Coding Practice ScreenThe Elice platform's practice feature, which is crucial for teaching coding, is one of the primary reasons the Busan Education Research & Information Institute selected it. Students can apply the theories they learn in the practice window to gain practical programming abilities in addition to studying them. On the platform, you can immediately solve the practical issues found in the real school information textbook.Elementary school entry practice on Elice LXP ▲ Entry Practice Screen on Busan EduOne 2.0 Real-time practice of the entry you learn should be available in elementary school. Open source was installed by Elice LXP in order for the entry to be used with the Busan Eduone 2.0 platform. Furthermore, it translated the material from published textbooks for elementary school students. Using it in public school was unavoidably convenient since it allowed for simultaneous textbook practice on a platform where instruction was provided, eliminating the need for separate enrollment and entrance website access.Feature of the LMS to make teaching easierTeachers praised Elice's Learning Management System (LMS) feature greatly. This is due to the fact that it can not only methodically oversee the educational process but also save educators time by enabling students to monitor their own learning progress, evaluate artificial intelligence, and automatically grade assignments. Since everyone can learn in the same setting and no complicated settings, such as downloading programs separately, are required, it was an ideal platform for educational material. Ongoing training facilitates the production and management of content ▲ Online teacher training conducted at Busan Eduone 2.0It was also highly accepted that information teachers receive ongoing training. No matter how excellent the platform is, it won't be of any use if it isn't used correctly, thus I made multiple attempts to optimize its use by speaking with teachers directly through both online and offline training. We have offered platform instructions, many templates, and markdown tutorials to assist teachers in creating content and practicing themselves, in addition to onboarding, which teaches the various features of the site. When it came to offline training, it also helped to get teachers together to build lecture materials that would have been challenging to produce separately.From creating and managing offline coding contess ▲ The "Summer Coding Festival" offline competition is to take place at the Elice Lab Busan CenterThe Busan Education Research & Information Institute held the Summer Coding Festival, in which Elice took part in a coding competition. The goal of the Summer Coding Festival is to foster critical thinking in computers through hands-on experience, as opposed to only serving as a competition. After finishing the pre-training at Busan Eduone 2.0, we participated in an offline coding competition where we had to answer challenges involving computational thinking, block coding, and text coding practice. Elice worked together from problem-solving to operation, which helped the competition come to a successful finish.High levels of satisfaction among field teachers and studentsThe ease with which theory instruction may be coordinated with practice and learning on a single platform was expressed by the teachers I interacted with during the training. Students were very interested in the ChatGPT-based AI Help chatbot. The process of asking questions and receiving replies from AI chatbots about the sections they didn't know generated a lot of fascinating reactions, and it was helpful because they could just ask questions straight on the platform.With the launch of the Elice platform, the instructors in Busan were able to provide more effective and superior instruction, and students were able to participate in more engaging and hands-on learning opportunities. Visit the following link to discover more about the Elice LXP, which will satisfy educators and students alike.👉 Do you want to know more about Elice LXP?👉 Get consultation on the right platform for your institution. *Elice owns the copyright to this content, which is protected under copyright law.*Without prior consent, secondary processing and commercial use of the content are prohibited.

12/23/2025

LG AI Research Validates Model Inference Performance and Stability on an NVIDIA B200 128‑GPU Cluster

Elice Cloud provides high‑performance AI infrastructure that enables large enterprise customers to validate their environments at real research scale. Among its offerings, the B200‑based high‑performance GPU clusters deliver an optimized environment for large‑scale model training and inference, engineered to reliably handle massive computational workloads.LG AI Research, one of only five teams selected for Korea’s independent AI foundation model initiative, ran both its EXAONE 4.0 32B model and leading global models on this infrastructure. The goal was to confirm whether Elice Cloud could fully meet the demanding requirements of a top‑tier research organization. Rather than a simple benchmark, this project faithfully replicated LG AI Research’s real‑world research environment, making it a meaningful end‑to‑end validation of large‑scale model operations.→ Explore how NC AI validated B200 performance on Elice Cloud for its independent AI foundation model initiativeValidation Objectives and ApproachElice Cloud partnered with LG AI Research to rigorously evaluate inference performance and operational stability in a next‑generation model research environment. The core goals of the project were to verify whether Elice Cloud can reliably handle large‑scale models, and to ensure that the infrastructure can scale to future research and production services without operational bottlenecks. To this end, the team composed test scenarios that closely mirrored real‑world research conditions by combining a wide range of factors, including long input sequence lengths, multiple precision modes, and different framework configurations, in order to objectively assess the reliability of the platform as a whole.Because research infrastructure cannot be judged by model performance alone, the team also examined potential issues across multiple stages of operation: VM creation and reconfiguration flows, internal and external network request handling, disk I/O stability, and InfiniBand configuration, among others. In large‑scale model research environments, not only inference latency and throughput but also the smoothness of the entire operational workflow is critical.Global Cloud Provider’s H200 Cluster vs. Elice Cloud’s B200 ClusterThe project ran for roughly three weeks, from October 2 to 19, 2025. Tests were conducted on a 128‑GPU NVIDIA B200 cluster, using a 128‑GPU H200 cluster from a global cloud provider as the primary point of comparison. LG AI Research validated both performance and runtime stability by running representative LLMs, including EXAONE, on vLLM‑ and SGLang‑based stacks. A 128‑way B200 cluster is a scale that even major research labs rarely get to use in practice, so the very act of operating real models at this magnitude served as a strong indicator of Elice Cloud’s ability to run and manage large‑scale GPU infrastructure.The team also varied input and output lengths by model to analyze how TTFT (time to first token) and throughput changed under different conditions. This ensured that the platform was not just fast in a few narrow benchmark scenarios, but could deliver consistent performance across the diverse input patterns encountered in real‑world workloads. For large‑scale research environments, earning trust in a project requires demonstrating that the system operates reliably across a wide range of conditions—not just at peak numbers under ideal settings.Up to 2.5× Higher PerformanceAcross the test suite, the B200 environment delivered roughly 1.5× to 2.5× higher performance compared to the H200 baseline. For EXAONE 4.0 32B on vLLM, TTFT was about 1.5× to 1.9× faster, while throughput was approximately 1.9× to 2.4× higher. Performance remained stable even with long input sequences, and the system maintained consistent processing speed as model sizes increased. For LLM Model A (SGLang), TTFT improved by about 1.6× and throughput by around 1.7×. For LLM Model B (vLLM), throughput improved by more than 2.5×, highlighting the architectural advantages of the B200 for inference workloads.From a stability perspective, GPU temperatures stayed mostly below 60°C, and no performance degradation or interruptions were observed due to thermal issues. This is a critical evaluation factor for research organizations that must run large models continuously over long periods. The fact that stability remained similar across different frameworks also underscores that the environment can be used consistently across diverse operational setups.Validation Results from a Research Infrastructure Operations PerspectiveFrom an operational standpoint, the team evaluated the network, storage, and VM lifecycle together. During large‑scale data uploads and model execution, the environment delivered the required level of network performance and stability, and on the internal network it was possible to run experiments without major issues even under heavy request loads. When connecting to external networks, there were a few instances of dropped requests; however, these were classified as requiring separate analysis, as they may be attributable to a mix of factors such as differences between cloud providers’ environments or characteristics of the network path.Disk performance on NVMe volumes met the read/write throughput required for large‑scale model inference. During the initial infrastructure setup, some parameter tuning was needed for the container runtime and InfiniBand networking, but after full‑scale experiments began, there were no significant operational issues caused by disk bottlenecks or network latency. The team documented several items as improvement points for real‑world operations—such as VM recreation flows, storage and network interface re‑assignment, and console session time limits—while also observing that overall operational efficiency improved over time, including reduced boot times compared to the initial state.Elice Cloud’s B200 Cluster, Proven Stable for Enterprise‑Grade Research OrganizationsThis project went beyond simply measuring large‑model inference performance; it demonstrated that Elice Cloud can meet the scale and conditions required by enterprise research organizations in real production‑like environments. Together with LG AI Research, the team achieved consistent results across a variety of models, precision modes, and framework configurations, and confirmed that performance remained stable even under realistic workloads such as long sequence inputs.In particular, the fact that a 128‑GPU B200 cluster operated stably under real large‑model workloads shows that Elice Cloud’s infrastructure is more than sufficient as a foundation for both research and production services. The infrastructure’s credibility is further strengthened by the fact that it has been directly validated by a large enterprise customer. Through this project, Elice Cloud once again proved its ability to operate large‑scale model research environments and will continue to deliver reliable, high‑performance AI infrastructure to leading research organizations and enterprises across industries.

11/28/2025

Corporate AI Training

No More AI Training That Doesn’t Translate to Real Work

AI training that stops at tool tutorials isn't enough

Training that does not translate into real work

One program with no audience-level design

Training that cannot be applied in secure environments

One-off sessions with no follow-through

AX Environment Connecting Training, Platform, and Infrastructure

Practice Environment Isolated from the Outside

AI Development Environment Identical to Actual Work

Enterprise AX Agents

Customized AX Training Roadmap Tailored to Organizational Goals

If You Want to Solve Problems Based on Your Organization’s Data

If You’re Planning or Expanding an Organization-wide AX Roadmap

If You Want to Quickly Apply the Latest AI Trends to Real Work

Tailored training for every level, from executives to key talent

Executive program

Team lead program

Company-wide program

Key talent program

Six training programs designed around your organization

Tailored AX Training Management for Organizations

Needs Analysis

Curriculum Design

Lab Environment Setup and Operation

Adoption at Work

AX that doesn't stop at training

Identify AI initiatives

Model development

Model enablement training

Agent deployment

Identify AI initiatives

Model development

Model enablement training

Agent deployment

Proven Training Partner, Elice

Hyundai Motor Company Namyang R&D Center: Building a 99%-Accurate Image Search and Classification System for Crash Testing

Soombit AI Trains on 14 Million Medical Images, Pursuing Korea’s First Regulatory Approval for a Generative AI Medical Device

An app that allows students and teachers to learn coding that they both find satisfactory

LG AI Research Validates Model Inference Performance and Stability on an NVIDIA B200 128‑GPU Cluster

Turn your organization’s AI capability into measurable results with a trusted partner

Hyundai Motor Company Namyang R&D Center: Building a 99%-Accurate Image Search and Classification System for Crash Testing

Soombit AI Trains on 14 Million Medical Images, Pursuing Korea’s First Regulatory Approval for a Generative AI Medical Device

An app that allows students and teachers to learn coding that they both find satisfactory

LG AI Research Validates Model Inference Performance and Stability on an NVIDIA B200 128‑GPU Cluster