“How could a small company build a world-class large-scale AI language model?”

How did a small startup build a large-scale language model?

This is the most frequently asked question after Clevi released its 1.4 trillion parameter Foundation model.

Unlike many companies that simply fine-tune open-source models, Clevi independently carried out every step from data collection, model design, large-scale distributed training, to quality verification.

The secrets and competitive advantages are explained in detail below.

1. Can a small company build a large-scale language model?

In July 2025, when Clevi unveiled its self-developed large-scale language model to the market, countless customers (users) were abuzz with surprise and skepticism. “Was it really self-developed?” “Isn’t it just a slightly modified open-source model?”

In fact, many companies at home and abroad simply perform transfer learning (fine-tuning) on open-source models and promote them as their own.

However, Clevi put forward the ‘From-scratch Foundation Model’.

That is, every process from data collection, model design, large-scale distributed training, to quality verification was directly designed and executed.

2. Why is developing a large-scale language model difficult?

To train a From-Scratch Foundation Model, all of the following items must be learned.

A large, high-quality dataset is required

Diversity: Ensuring diversity in language, domain, and format (text, image, etc.)
Refinement: Noise removal, quality control, duplicate/harmful data filtering
Licensing: Clarifying copyright and data usage rights

Large-scale computing infrastructure

High-performance GPU/TPU clusters: Technology to support large-scale distributed training by integrating thousands to tens of thousands of nodes
Efficient distributed training frameworks: DeepSpeed, Megatron-LM, Ray, etc.
Storage/Network: Large-scale data input/output, fast network environment

Model architecture design

Incorporating latest structures: Transformer, MoE (Mixture of Experts), multimodal, etc.
Scalability: Considering expansion of parameter count, layer count, input length, etc.
Efficiency: Optimizing training/inference speed, memory usage

Training strategies and algorithms

Pretraining objectives: Language models (e.g., next token prediction), multimodal (e.g., contrastive learning), etc.
Optimization techniques: mixed precision, gradient accumulation, learning rate schedule, etc.
Normalization/Stabilization: dropout, layer norm, weight decay, etc.

Evaluation and verification system

Benchmark sets: Using standard datasets (Benchmarks)
Internal evaluation: Evaluation based on real usage scenarios
Continuous monitoring: Checking quality, bias, and safety during/after training

Human resources and organizational capabilities

AI/ML researchers: Model design, algorithm development
Data engineers: Data collection/refinement/management
Infrastructure engineers: Distributed systems, cloud/on-premises management
Project managers: Schedule, budget, quality management

Ethical, legal, and security considerations

AI ethics: Bias, safety, transparency, accountability
Legal compliance: Personal information, copyright, data sovereignty
Security: Preventing data/model leakage, access control

Currently, there are about 2,000 AI researchers worldwide, and most of them are concentrated in big tech companies like META, Google, and XAI. In Korea, there are very few teams capable of designing everything from data collection to large-scale training infrastructure to build a Foundation model.

3. Building a large-scale language model with a small team.

However, Clevi CEO Hwanho Lee decided to build the world’s best AI at “Clevi.”

Based on over 10 years of research in deep learning and machine learning, CEO Hwanho Lee founded the company (Clevi) three years ago with the goal of “building the world’s best AI.”

By directly designing every process—building the company’s own data pipeline, designing large-scale distributed deep learning infrastructure, developing model architecture, and quality verification—Clevi acquired the capabilities needed to develop a Foundation Model.

After training the model several times, Clevi now possesses the Clevi-5-x model, which can compete with the world’s top models.

To effectively train a large-scale language model, hundreds to thousands of GPUs must be connected in a cluster to process calculations reaching quintillions per second simultaneously. In this process, technology to minimize computation synchronization and communication delays in a large-scale distributed environment is key. Until now, most companies in Korea have not been able to train models properly due to the lack of “precision control algorithms.” By developing such a proprietary distributed computation control algorithm, Clevi CEO Hwanho Lee succeeded in training a large-scale language model with 1.4 trillion parameters for the first time in Korea.

4. Why could a small team develop various models?

Most big tech companies also have large-scale infrastructure control and data refinement technologies.

To manage this, hundreds to thousands of researchers are needed.

However, Clevi develops and possesses various models with a small research team.

How was this possible?

Clevi develops and possesses various models with a small team through Teacher-Student Model (Knowledge Distillation) technology.

Let’s take a look at Teacher-Student technology.

Teacher-Student (Knowledge Distillation)

The Teacher-Student technology, simply put, is a technique where a large-scale language model (Teacher Model) evaluates and provides feedback to child models (Student Models), allowing a small team to quickly develop various models as if replacing hundreds of researchers.

The Mother Model (large-scale model) evaluates and provides feedback on models in various fields, replacing hundreds of researchers.
With this method, if learning is conducted through the Clevi-x-platform, high-performance models such as Reasoning, VLM, Physical AI, and Coding Agent can be trained and deployed in just 10–15 days.

Clevi Coding Agent

Clevi Physical AI Learning Platform

VLM-Vision Language Model

Security Agent

5. Clevi-x-platform’s technological and quality differentiation

Model performance and scalability

Superiority in inference (CoT/Reasoning): cip-5-x incorporates step-by-step logical development and evidence presentation into its design philosophy, demonstrating highly reliable calculation and interpretation abilities in solving science, math, and engineering problems. According to internal benchmarks, it is designed and operated with the goal of achieving performance equivalent to or better than GPT-5, and reproducibility/verification under the same prompt conditions is managed as a core quality indicator.

Full-spectrum multimodal: Purpose-driven VLM (cip-5-vision), large-scale multimodal processing model (ivy-4-mm), and lightweight on-device (ivy-3-text) can be produced and combined on a single platform, enabling optimal combinations tailored to task characteristics (search/classification/summarization vs. reasoning/explanation/execution).

Enterprise applicability

On-premises security and availability: Operates within the entire closed network (input-training-service-backup), HA design, modular expansion, and SVCE (isolated safe execution environment) to separately protect data and model parameters.
Rapid adoption and expansion: Ivy Chat (business-type multimodal conversation/agent), API Platform (global standard SaaS) support quick application and operation.

Physical AI differentiation

Combining NVIDIA Isaac-based simulation and evolutionary reinforcement learning (Evolutionary RL), Sim2Real transfer and parallel learning with synthetic data improve learning efficiency and field adaptability. Covering everything from digital-robotics learning to deployment is rare among alternatives.

Service quality and governance

Model/prompt/tool version management, evaluation suite (Korean/English knowledge, industry-specific tasks, hallucination/safety indicators), change management (SLO/SLA), and operational monitoring (latency, success rate, TCO) are managed through a single platform process.

Currently, there are over 300 AI model service companies in Korea,

but Clevi is the only one that can provide both on-premises and cloud services through a high-performance proprietary foundation model.

6. How to utilize language models in industry

Since AI adoption requires massive investment and thorough verification, it is essential to directly compare and experience whether it is truly a helpful solution for the enterprise.

Clevi not only builds models but also provides AI solutions applicable to real industrial sites.
For example, Ivy Chat Platform, Clevi API Platform, industry-specific customization, and security requirements for enterprise channels are fully equipped.
It possesses technical capabilities rarely found at home or abroad, such as physical AI, robotics, and multimodal data processing.

Clevi provides AI not as a ‘goal’ but as a ‘means,’ delivering real AI solutions that contribute to actual work efficiency and industrial innovation. Experience the true differentiation of a From-scratch Foundation model for yourself.

From-scratch Foundation Model_Clevi AI(ivy)