GPU Cluster Solution
NVIDIA DGX, HGX and PCIe GPU cluster design, installation and management. AI training, inference and scientific computing infrastructures.
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.
The difference between a good and a poorly integrated GPU cluster often appears in scaling efficiency: NVLink and InfiniBand must work together so training throughput grows with card count. Mevasis validates this balance with benchmarks before handover.
Key Features
Multi-Node GPU Infrastructure
We design high-capacity GPU clusters based on NVLink-enabled NVIDIA DGX H100/H200 and HGX platforms. NVLink and NVSwitch fabrics let GPUs communicate at extremely high bandwidth, essential for large model training. The cluster is sized and connected for your training and inference workloads.
High-Speed Network Integration
We deploy network infrastructure that minimizes inter-node latency using InfiniBand NDR (400 Gbps) and RoCE v2 technologies. This is critical for distributed training jobs where GPUs must exchange gradients frequently across nodes. The fabric is designed so bandwidth scales with the number of nodes.
Intelligent Workload Scheduling
We provide the most appropriate resource management for your workload with SLURM and Kubernetes plus GPU Operator options. SLURM suits classic HPC batch workloads, while Kubernetes handles containerized AI pipelines and CI/CD integration. GPUs are allocated, shared and isolated as your teams need.
End-to-End Monitoring
We monitor GPU metrics in real time with DCGM Exporter, Prometheus and Grafana. Temperature, power, utilization and memory are tracked per GPU, and alerting catches anomalies before they disrupt training. Historical data also supports capacity planning and troubleshooting.
Frequently Asked Questions
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.
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.
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.
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