NVidia HPC&GPU Systems

In almost all cases — yes.
AI training and HPC workloads require very low latency and extremely high bandwidth.

InfiniBand provides:

• Up to 400–800 Gbps throughput
• Ultra-low latency
• Efficient collective GPU operations without bottlenecks

Without it, cluster performance is often only partially utilized.

It depends on the workload:

• NVIDIA H200 / B300 — large-scale LLM training and HPC simulations
• RTX 6000 PRO — mid-scale AI workloads and visualization
• RTX 6000 PRO — inference, computer vision, enterprise AI platforms
• DGX systems — full supercomputer-class performance with an optimized software stack

Datakom helps select the optimal GPU mix based on your use case and budget.

Yes — this is a core competency.
GPU servers are only one part of the system. We configure:

• InfiniBand networks
• NVIDIA Spectrum-X Ethernet
• High-performance storage (NVMe-oF, Lustre, GPFS)

Only a properly designed infrastructure delivers full GPU performance.

Yes — using:

• Nutanix
• NVIDIA AI Enterprise
• Kubernetes
• Slurm
• Red Hat OpenShift

These platforms enable:

• GPU resource sharing
• Multi-user access
• Security and auditing
• Workload prioritization

This allows one cluster to be used by data scientists, developers, and research teams simultaneously.

GPU clusters consume significant power, especially for LLM training.

Typical requirements:

• 2–10+ kW per node
• Optimized cooling architecture (air or liquid)
• Stable power supply, UPS, and capacity planning

Datakom performs full power and cooling calculations before deployment.

Both options are possible, but on-premises HPC offers:

• Lower long-term costs
• Full data control
• No connectivity limitations
• Better performance for sustained workloads
• Predictable costs without “surprise billing”

Many organizations use a hybrid model: on-prem HPC with cloud bursting.

Depending on project scope:

• 2–6 weeks — architecture and integration planning
• 1–3 weeks — network and storage deployment
• 1–2 weeks — GPU node configuration and testing

Total: approximately 4–10 weeks.

Yes — we offer full lifecycle support:

• Proactive monitoring (GPU utilization, network throughput, temperature)
• Security management
• Regular performance audits
• 24/7 incident response

Our goal is minimal downtime and maximum HPC efficiency.

Yes — HPC is not only for large enterprises.

Available options include:

• Compact 1–2 node GPU systems
• Mid-sized clusters for analytics
• Virtualized environments with shared GPU access

Many customers start small and scale as needs grow.

Yes — they are versatile high-performance platforms for:

• Scientific simulations
• Engineering modeling (CAE, CFD, FEM)
• Data visualization
• Medical and bioinformatics workloads
• Telecommunications and IoT data processing

We ensure customers receive a fully usable HPC environment — not just hardware.