AI Cloud
H100 on Demand, AI Development Without Delays | LG U plus
2026.06.30.
■ Global Cloud Wait Times: A Major Risk to PoC Schedules
Developing an internal AI platform requires high-performance GPU infrastructure capable of handling large-scale workloads reliably. During the proof-of-concept (PoC) stage for foundation models such as NVIDIA Cosmos, securing enough H100 GPUs at the right time can determine whether a project stays on schedule.
While building its NVIDIA Cosmos-based internal AI platform, LG Uplus encountered infrastructure challenges. Its existing on-premises environment could not meet the demands of large-scale computing, while building GPU clusters through global cloud service providers (CSPs) involved long provisioning times and significant infrastructure costs. These constraints made it difficult to manage both project timelines and PoC budgets efficiently.
To address these challenges, LG Uplus adopted Runyour AI. By providing immediate access to large-scale H100 GPU resources without provisioning delays, Runyour AI established a foundation that allowed the company to manage both project schedules and budgets more effectively.
■ From Instant GPU Access to Lower Infrastructure Costs
■ Building a Faster Foundation for AI R&D
After adopting Runyour AI, LG Uplus reduced project timelines by 60% while lowering infrastructure costs by 20–40%. By minimizing delays in infrastructure provisioning and environment setup, the company improved operational efficiency and kept development of its NVIDIA Cosmos-based internal AI platform on schedule.
The LG Uplus project demonstrates that Runyour AI is more than a source of GPU capacity. By eliminating both infrastructure wait times and cost barriers, it provides a practical AI infrastructure foundation that enables organizations to move large-scale PoCs forward while allowing research and development teams to focus on their core work.
Developing an internal AI platform requires high-performance GPU infrastructure capable of handling large-scale workloads reliably. During the proof-of-concept (PoC) stage for foundation models such as NVIDIA Cosmos, securing enough H100 GPUs at the right time can determine whether a project stays on schedule.
While building its NVIDIA Cosmos-based internal AI platform, LG Uplus encountered infrastructure challenges. Its existing on-premises environment could not meet the demands of large-scale computing, while building GPU clusters through global cloud service providers (CSPs) involved long provisioning times and significant infrastructure costs. These constraints made it difficult to manage both project timelines and PoC budgets efficiently.
To address these challenges, LG Uplus adopted Runyour AI. By providing immediate access to large-scale H100 GPU resources without provisioning delays, Runyour AI established a foundation that allowed the company to manage both project schedules and budgets more effectively.
■ From Instant GPU Access to Lower Infrastructure Costs
- Immediate access to large-scale GPU resources: With access to more than 400,000 GPU nodes, Runyour AI enabled large-scale H100 resources to be provisioned without bottlenecks or waiting periods. Eliminating the provisioning delays common with global cloud providers allowed LG Uplus to begin infrastructure operations while keeping its original PoC schedule on track.
- Ready-to-use infrastructure from day one: Infrastructure configuration can become another source of delay during a PoC. Runyour AI minimized that risk by providing up-to-date AI templates with container operating systems and CUDA drivers already configured. As a result, the team was able to begin validating NVIDIA Cosmos immediately without additional environment setup.
- A cost structure designed for large-scale PoCs: Large-scale H100 infrastructure is often one of the biggest expenses during a PoC. Runyour AI reduced cloud costs by 20–40% compared with major global CSPs, significantly lowering the financial burden of deploying large GPU environments. This allowed LG Uplus to maintain the infrastructure capacity it needed while managing its PoC budget more efficiently.
■ Building a Faster Foundation for AI R&D
After adopting Runyour AI, LG Uplus reduced project timelines by 60% while lowering infrastructure costs by 20–40%. By minimizing delays in infrastructure provisioning and environment setup, the company improved operational efficiency and kept development of its NVIDIA Cosmos-based internal AI platform on schedule.
The LG Uplus project demonstrates that Runyour AI is more than a source of GPU capacity. By eliminating both infrastructure wait times and cost barriers, it provides a practical AI infrastructure foundation that enables organizations to move large-scale PoCs forward while allowing research and development teams to focus on their core work.
