The Most Seasoned Partner for the AI Era, AIMTOG

KOREAN

AI DATA CENTER · 01

AI infrastructure
that determines compute performance

The performance of an AI workload comes from the balance of compute, data, and network.

AI Business Stack

How AI actually works

An AI business is built from multiple layers stacked on top of one another.
Whatever the upper layers do, what holds them up is the physical foundation beneath.

  • AI AgentApplication layer Business automation, service integration, and real-world business use

  • ModelModel layerTraining, inference, fine-tuning, and model serving

  • PlatformPlatform layerCluster orchestration, virtualization and container environments, and MLOps integration

  • HardwareInfrastructure layerCompute, storage, and network — the physical substance of AI computation

    Global partners
    1. HPECompute · Storage · Network
    2. NVIDIAGPU Computing
    3. NetAppStorage
    4. Pure StorageAll-Flash
    5. NutanixHCI
    6. ArcfraHCI & SDS

  • FacilityFoundation layerPower, cooling, and floor space — the physical base on which every layer rests

Every AI service is ultimately computed on servers and storage, and AIMTOG maintains a portfolio spanning multiple global vendors.

Infrastructure Stack

AI infrastructure organized into three layers

The hardware layer splits into three branches. None exists in isolation — AIMTOG designs each configuration around the balance between them.

  • LAYER 01

    Compute layer

    The layer where the AI workload actually runs. We combine general-purpose and accelerated compute to match each workload, securing exactly the processing power required.

    • GPU-accelerated compute nodes
    • General-purpose x86 server infrastructure
    • Multi-node cluster configuration
    • Virtualization and container environments
    • Scale-out node architecture
    • Configuration tuned for power and heat

  • LAYER 02

    Data layer

    The layer that feeds data to compute. The design goal is to secure enough throughput that the GPUs never sit idle waiting.

    • All-Flash high-performance storage
    • Large-scale training-data storage
    • HCI integrated infrastructure
    • Tiered storage configuration
    • Data-pipeline optimization
    • Backup and replication framework

  • LAYER 03

    Network layer

    The foundation that connects layers and nodes. In distributed training, node-to-node communication speed drives overall performance, so we weigh bandwidth and latency together.

    • High-bandwidth cluster interconnect
    • Separation of compute and storage networks
    • Low-latency communication design
    • Topology designed for scale
    • Traffic-flow optimization
    • Redundant configuration

By Workload

The configuration changes with the workload

Even within the same layered structure, what you process changes how resources are allocated.
AIMTOG confirms the customer's workload first, then proposes a configuration.

  • WORKLOAD 01

    Training

    Iterative computation of a large-scale model, with many nodes working in parallel. Total compute volume and node-to-node communication determine performance.

    Design focus

    compute density · inter-node bandwidth · data-feed throughput

  • WORKLOAD 02

    Inference

    The stage where a trained model responds as a live service. Response speed and stable concurrency matter more than raw compute volume.

    Design focus

    response latency · concurrency · service availability

  • WORKLOAD 03

    Data processing

    The pre-training stage of collecting, cleaning, and transforming data. It depends on storage capacity and I/O performance more than on compute.

    Design focus

    storage capacity · I/O performance · pipeline integration

How We Build

From configuration to bring-up

We don't just hand over an equipment list. We confirm the workload, design the configuration, and deliver it in a state that actually runs.

  • STEP 01

    Confirm the workload

    We identify the nature and scale of the work to be processed, along with growth plans.

  • STEP 02

    Design the configuration

    We derive a configuration reflecting the balance between layers and the power and space conditions.

  • STEP 03

    Build and deploy

    We install the equipment, configure the cluster, and deploy the software stack.

  • STEP 04

    Validate and hand over

    We validate performance and hand over the system with the workload running normally.

We'll review which configuration fits — together with you.

Ask about infrastructure design