When AI initiatives scale, infrastructure becomes speed—and trust
WITIVE is actively researching, building, and commercializing multiple AI-driven projects. As the number of initiatives grows, we need on-demand compute, a reproducible standard environment, production-grade stability, and scalability.
Securing NVIDIA L40S and deploying the ASUS ESC4000-E11 is not just adding hardware—it is upgrading the foundation that connects R&D to production with higher consistency and momentum.
1. System Overview — ASUS ESC4000-E11 GPU Server
The ASUS ESC4000-E11 is a 2U rackmount dual-socket platform built for enterprise AI/HPC workloads.
Key highlights include:
– 2 × Intel® Xeon® Scalable (LGA4677)
– Up to 16 DIMMs, ECC RDIMM DDR5-5600 (TB-class scalability)
– Up to 6 × NVMe/SATA/SAS hot-swap bays
– 1+1 redundant 2600W (80 PLUS Titanium)
– 2 × 1GbE + management port, OOB management (ASMB11-iKVM)
It is designed to balance performance, reliability, and operational manageability.
2. Current Installed Configuration — Dual Xeon Gold 6430 + L40S 48GB
The current WITIVE server is configured with the following specifications.
✔ CPU : Intel Xeon Gold 6430 × 2 (Dual-socket)
✔ GPU : NVIDIA L40S 48GB × 1 (AI GPU optimized for inference and service workloads)
✔ RAM : DDR5-5600 ECC RDIMM 32GB × 2 = 64GB
✔ Storage : NVMe 4TB
✔ Power : 2600W × 2 Redundant (80 PLUS Titanium)
While the initial setup starts with 64GB of memory, the platform itself is designed with large-scale expansion in mind, allowing flexible response to future workload growth.
3. Impact — Faster iteration and more consistent production quality
AI progress depends on rapid iteration. Internal GPU capacity reduces wait times and improves throughput from PoC to MVP to production.
In production, stability matters as much as model quality—latency, concurrency, recovery, deployment/rollback, and monitoring. This upgrade strengthens both “speed of learning” and “reliability of delivery.”
4. WITIVE’s AI initiatives — CONNECT WORKS, G2B AI, U:CON, and beyond
WITIVE continues to advance CONNECT WORKS, G2B AI (public procurement intelligence), and U:CON (multi-tenant travel distribution OS), alongside multiple ongoing R&D efforts.
This GPU server is not for a single product—it provides a standardized execution environment to reduce bottlenecks across parallel projects, improving productivity and delivery predictability.
5. Next Step — Considering a micro data center (45 pyeong × 2 units)
WITIVE has already acquired 45-pyeong-scale units located in knowledge-industry centers at Daedeok Biz Center (Gwanpyeong-dong) and Gaon Biz Tower (Daehwa-dong), and is currently reviewing the construction of a small-scale data center utilizing these spaces.
For AI and platform companies, a data center is not merely a physical facility, but an operational system that ensures stable and scalable service delivery.
– Infrastructure scalability including rack layout, power capacity, cooling systems, and cabling
– Redundancy design such as dual power and network paths, UPS, and network lines
– Access control, security policies, and hardware permission management
– Monitoring, maintenance, and incident response processes
The introduction of GPU servers marks the starting point of this infrastructure strategy. Going forward, WITIVE aims to move beyond reactive, ad-hoc responses and instead build a sustainably operable growth structure through phased, plan-driven expansion.
Author’s Note
