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AWS Software / SaaS South Korea

LG AI Research

Application Refactoring

, the artificial intelligence (AI) research hub of South Korean conglomerate LG Group, was founded to promote AI as part of its digital transformation strategy to drive future growth.

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EXAONE, which stands for "expert AI for everyone," is a 300-billion-parameter multi-modal model that uses both images and text data.LG AI Research used Amazon SageMaker to train...
In addition, by leveraging the parallel data library from Amazon SageMaker, we could obtain training results quickly as the number of GPUs and model parameters increased."LG AI...
It also increased the data processing speed by about 60 percent using the Amazon SageMaker distributed data parallel library.
Key Outcomes to develop the EXAONE AI engine 1 year that supports linear scaling Scalability reduction in cost of building AI engine 35% increase in data preparation speed 60%

Reported impact

Business outcome

Using EXAONE, LG AI Research developed an AI virtual artist called Tilda. The fundamental power of Tilda's artistic qualities comes from EXAONE, which was trained using 600 billion pieces of artwork and 250 million high-resolution images accompanied with text.

  • Cost Reduction
  • Scalability
  • Speed to Market

Solution

LG AI Research , the artificial intelligence (AI) research hub of South Korean conglomerate LG Group, was founded to promote AI as part of its digital transformation strategy to drive future growth. The research institute developed its foundation model EXAONE engine within one year using Amazon SageMaker and Amazon FSx for Lustre. Built on Amazon Web Services (AWS), the foundation model mimics humans as it thinks, learns, and takes actions on its own through large-scale data training. The multi-purpose foundation model can be employed in various industries to carry out a range of tasks. LG AI Research successfully deployed its foundation model, EXAONE, to production in one year. EXAONE, which stands for "expert AI for everyone," is a 300-billion-parameter multi-modal model that uses both images and text data.LG AI Research used Amazon SageMaker to train its large-scale foundation model and Amazon FSx for Lustre to distribute data into instances to accelerate model...

Data flow

It also increased the data processing speed by about 60 percent using the Amazon SageMaker distributed data parallel library.

Solution architecture

2 components · 2 layers
  1. Compute
    • Amazon SageMaker Builds, trains, or deploys the machine learning models behind the modernized application workflows.
  2. Storage
    • Amazon FSx for Lustre Provides high-throughput file storage for the modernized application workflows.

Evidence from the source

Built on Amazon Web Services (AWS), the foundation model mimics humans as it thinks, learns, and takes actions on its own through large-scale data training.
Considering all the challenges in an on-premises solution, LG AI Research decided that Amazon SageMaker was the best fit for this project.
Customer Quote By using Amazon SageMaker's high-performance distributed training infrastructure, researchers can focus solely on model training instead of managing infrastructure." Kim Seung Hwan Head of LG AI Research Vision Lab
Moreover, the project required a high-performance compute infrastructure and the flexibility to increase the number of parameters to billions during training.Workflow automation was also important, as multiple models or downstream tasks...
The research institute developed its foundation model EXAONE engine within one year using Amazon SageMaker and Amazon FSx for Lustre.
The research institute developed its foundation model EXAONE engine within one year using Built on Amazon Web Services (AWS), the foundation model mimics humans as it thinks, learns, and takes actions on its own through large-scale data...
Transparency Evidence & methodology 81/100 reference value · 87% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value81/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth15/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity16/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    2 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    6 direct source quotes retained.
  • Date provenance8/10
    The publication date comes from publisher-controlled metadata.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. AWS

    LG AI Research customer story

    First-party
    https://aws.amazon.com/solutions/case-studies/lg-ai-research-case-study/
    Source type
    Vendor-published customer story
    Published
    Mar 1, 2023
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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