H100 and A100 accelerators assigned whole, never shared, for training and inference.
Overview
Use the entire GPU server to yourself. With GPU and memory never shared and no virtualization overhead, bare metal delivers the full performance of your training and inference workloads.
The GPU and host are entirely yours: consistent performance with no noisy-neighbor interference.
Power delivery and precision cooling matched to high-wattage GPUs keep them running at full load without throttling.
24–141 GBVRAM
Memory (VRAM) sets the limit on model size. Check that your model fits first.
Images with the major frameworks and drivers let you start training right away, with no time spent on setup.
Multi-GPU builds use NVLink to secure inter-GPU bandwidth.
GPU lineup
Choose the GPU that fits your workload. Per-GPU pricing; multi-GPU builds are also available.
A100
80GB of memory and high bandwidth carry real training runs. Multi-card builds use NVLink for bandwidth between GPUs.
Good for
₩3,000,000/mo
Listed prices are estimates per GPU per month, VAT excluded. Quotes may vary by GPU supply, configuration and contract term.
Request consultationUse cases
Use multiple GPUs and high-bandwidth interconnect to train large language and generative models.
Deploy trained models as real-time services with low latency and steady throughput.
Fine-tune models on your own data and build RAG pipelines.
Accelerate render farms, simulation, and video processing workloads.
Features
01
GPUs are passed through in full, giving you complete performance with no sharing.
02
Fast NVMe reduces bottlenecks when loading large datasets.
03
Hardware faults are caught by 24-hour monitoring and handled; operations questions are answered Mon-Fri 10:00-18:00 KST.
04
Single-tenant physical isolation with access control and network security to protect your data.
No. You take the GPU and the host whole. It is passed through, so there is neither virtualisation overhead nor a noisy neighbour.
Yes. The table is priced per GPU; multi-GPU builds use NVLink for bandwidth between cards. Tell us how many you need and we quote accordingly.
You get an image with CUDA, the drivers and the usual frameworks such as PyTorch already in place. If you need particular versions we set those up.
It is an estimate per GPU per month, before VAT. Supply, build and contract length all move the number, so the firm figure comes from the conversation.
Yes. The racks are built for high-power GPUs with matched power delivery and precision cooling, so they run without throttling. Round-the-clock monitoring and spare capacity keep downtime short if hardware fails.
A GPU server is bare metal too. The difference is the accelerator. If the work is CPU-bound, a bare-metal server fits; if GPUs do the work, as in training and inference, this is the one. Tell us the workload and we will work it out with you.
It depends on the GPU model and on supply. Once the build is settled in a conversation, we confirm the delivery schedule with you.
The term is agreed with you. Unit pricing can differ with the length of the term.
Tell us how much your datasets and checkpoints need and we size the NVMe accordingly. For large training data we can plan separate storage alongside it.
The server is yours alone, so disks are never shared with another customer. Media is wiped when the server is returned, and we can agree the exact procedure with you in advance.
Tell us your workload and the GPUs you need, and we'll propose the optimal build and quote.
