GPU Cluster Solution
NVIDIA DGX, HGX and PCIe GPU cluster design, installation and management. AI training, inference and scientific computing infrastructures.
What is GPU Cluster Solution?
A GPU cluster is a distributed infrastructure that connects multiple GPUs over a high-speed network to form a single compute pool. It is critical for every workload where a single GPU falls short — from large language model training to scientific simulation. Mevasis delivers end-to-end GPU cluster design, installation and management, from NVIDIA DGX/HGX hardware through InfiniBand network integration to SLURM/Kubernetes workload scheduling.
A properly configured GPU cluster compresses the same training workload from weeks to days; this directly impacts both research velocity and total project cost.
— Mevasis HPC Engineering Team
How Is a GPU Cluster Built?
Mevasis delivers production-ready GPU cluster infrastructure quickly through a four-step methodology: from workload analysis and hardware installation to benchmark testing and team training.
Architecture Design
We analyze workload requirements and determine the GPU model, node count, network topology and storage capacity.
Installation and Validation
After hardware assembly and software stack installation, we validate performance with NCCL, HPL and MPI benchmark tests.
Handover and Ongoing Support
We provide team training and comprehensive documentation, plus optional maintenance agreements for continuous support.
Frequently Asked Questions
When should this solution be chosen?
A GPU cluster solution should be chosen for large-scale deep learning training, LLM fine-tuning, scientific simulation or high-volume inference workloads. GPU clusters are the right choice when the compute power of a single GPU is insufficient, when model sizes exceed a single card's memory, or when reducing training times is critical.
How does Mevasis deliver this solution?
Mevasis provides end-to-end GPU cluster design, installation and management — primarily on NVIDIA DGX and HGX systems but covering diverse GPU architectures — from hardware selection through InfiniBand/RoCE network integration, SLURM or Kubernetes-based scheduling, and a full monitoring stack. Our experienced engineering team determines the project-specific architecture and delivers a production-ready environment in a short timeframe.
How is pricing structured?
GPU cluster solutions vary by hardware configuration, network infrastructure, software stack and support scope, so pricing is project-specific. We recommend filling in our request form to receive an accurate quote; our team will evaluate your requirements and get back to you as soon as possible.