GPU Cluster Design & Integration — NVIDIA H100, InfiniBand Fabric & Liquid Cooling
AI & Compute
GPU Clusters
End-to-end GPU cluster design and integration — from 8-GPU development systems to 10,000-GPU hyperscale training clusters — with InfiniBand fabric, liquid cooling, and parallel storage.
Full-Stack GPU Cluster Expertise
Full-Stack Integration
We integrate the complete GPU cluster stack — servers, networking, storage, cooling, and power — as a single engineered system, not a collection of independently procured components.
InfiniBand & RoCE Expertise
Deep expertise in InfiniBand NDR fabric design, subnet manager configuration, and RoCE v2 tuning for optimal GPU-to-GPU bandwidth and minimal collective communication overhead.
Liquid Cooling Integration
Factory-integrated direct liquid cooling (DLC) manifolds, CDU installation, and thermal validation ensure GPU junction temperatures stay within spec under sustained training loads.
Power Infrastructure
480V 3-phase power distribution, high-efficiency PDUs, and 2N redundancy designed specifically for the power profiles of large GPU clusters.
Performance Benchmarking
MLPerf training benchmarks, NCCL all-reduce tests, and storage throughput validation confirm cluster performance before handover.
Operational Readiness
Cluster management software (SLURM, Kubernetes), monitoring stack, and runbook documentation delivered with every cluster build.
Scale
Cluster Tiers
Development Cluster
EntryModel development, fine-tuning, inference testing
GPU Count
8 – 64 GPUs
Power
80 – 640 kW
Cooling
Air or rear-door HX
Training Cluster
Most CommonFoundation model training, large-scale fine-tuning
GPU Count
64 – 1,024 GPUs
Power
640 kW – 10 MW
Cooling
Direct liquid cooling
Hyperscale Cluster
EnterpriseFrontier model training, national AI research
GPU Count
1,024 – 10,000+ GPUs
Power
10 MW – 100+ MW
Cooling
Immersion or DLC at scale
Technical Specifications
Frequently Asked Questions
What is the difference between InfiniBand and RoCE for GPU clusters?
InfiniBand NDR delivers 400 Gb/s per port with hardware-offloaded RDMA and is the gold standard for large-scale AI training. RoCE v2 runs RDMA over standard Ethernet and is more cost-effective for smaller clusters or inference workloads. We recommend InfiniBand for training clusters above 64 GPUs and RoCE v2 for inference or smaller training workloads.
How do you handle cooling for 130 kW racks?
At 130 kW per rack, air cooling is not viable. We install direct liquid cooling (DLC) manifolds inside each rack, connected to a facility-level coolant distribution unit (CDU). The CDU transfers heat to the building chilled water loop. We design the full hydraulic circuit from GPU cold plate to cooling tower.
What storage architecture do you recommend for AI training?
AI training requires high-throughput parallel storage — typically all-NVMe with a parallel file system (GPFS, Lustre, or WekaFS). We size storage based on dataset size, checkpoint frequency, and required read bandwidth. A typical 1,024-GPU cluster requires 200–500 GB/s of sustained read throughput.
Can you integrate with existing data center infrastructure?
Yes. We assess your existing power, cooling, and network infrastructure and design the GPU cluster integration to work within your constraints. If upgrades are required, we scope and deliver them as part of the project.
What cluster management software do you deploy?
We deploy SLURM for HPC-style job scheduling, Kubernetes with GPU operator for containerized workloads, and NVIDIA Base Command Manager for GPU fleet management. We configure monitoring with Prometheus, Grafana, and DCGM for GPU telemetry.
Why Organizations Act
Business Challenges We Solve
GPU Procurement Lead Times
H100 and H200 allocations stretch 6–18 months. Without a procurement partner with vendor relationships, AI projects stall before a single GPU is racked.
Cluster Networking Complexity
Multi-node GPU training requires InfiniBand or RoCEv2 at 400Gb/s. Misconfigured rail-optimized topologies destroy all-reduce performance and waste expensive GPU time.
Power Density Constraints
A single DGX H100 draws 10.2 kW. A 10-node cluster requires 100+ kW of dedicated power — most existing data centers cannot support this without significant infrastructure upgrades.
Cooling for High-Density Racks
Air cooling fails above 30–40 kW per rack. GPU clusters require direct liquid cooling (DLC) or rear-door heat exchangers, which most facilities are not pre-provisioned for.
Storage Throughput Bottlenecks
Training jobs require 100+ GB/s of sustained storage throughput. Inadequate storage architecture causes GPU idle time that inflates training costs by 30–50%.
Cluster Management & Orchestration
Kubernetes, Slurm, and NVIDIA Base Command require specialized expertise. Without proper job scheduling and resource isolation, cluster utilization rates fall below 60%.
Vendor-Neutral Expertise
Technology Ecosystem
DCS Global is vendor-neutral and works with the leading platforms in the industry. We recommend the right technology for your requirements — not the vendor with the best margin.
GPU Platform
Server Platform
Cluster Networking
Orchestration
Parallel Storage
Vendor-Neutral Advisory
DCS Global holds no exclusive reseller agreements that would bias our recommendations. Our engineers are certified across multiple platforms and will specify the solution that best fits your technical requirements, budget, and long-term roadmap.
Trusted Advisor Framework
GPU Cluster Buyer\'s Guide
Use this framework to evaluate your requirements before engaging vendors. Organizations that complete this analysis make faster decisions and achieve better outcomes.
What is your primary workload — training, fine-tuning, or inference?
Training large models requires maximum NVLink bandwidth and all-reduce performance. Fine-tuning can use smaller GPU counts. Inference prioritizes throughput per dollar, often favoring different GPU SKUs.
What is your target cluster scale — nodes, GPUs, and GPU memory?
Model size determines minimum GPU memory. GPT-4 class models require 1,000+ GPUs. Llama-70B can be trained on 64–128 GPUs. Cluster scale drives every infrastructure decision downstream.
Do you require bare-metal or cloud-burst capability?
Bare-metal clusters deliver maximum performance and data sovereignty. Cloud-burst capability adds flexibility for variable workloads but requires hybrid networking and security architecture.
What are your storage throughput and capacity requirements?
Training throughput requirements are calculated from model size, batch size, and GPU count. Undersized storage is the most common cause of GPU idle time and wasted training budget.
What is your power and cooling infrastructure baseline?
GPU clusters require 10–30 kW per rack. Facilities must be assessed for available power capacity, UPS headroom, and cooling capability before cluster design begins.
What is your cluster management and MLOps maturity?
Bare-metal clusters require operational expertise in job scheduling, container orchestration, and model lifecycle management. Gaps here reduce cluster utilization and increase time-to-insight.
Not sure where to start? Our solutions advisors can walk you through this framework in a 30-minute discovery call.
Schedule an Infrastructure AssessmentDecision Framework
On-Premises Cluster vs. Cloud GPU
Use this framework to evaluate whether building an on-premises GPU cluster or using cloud GPU instances is the right decision for your AI workloads.
| Criterion | On-Premises GPU Cluster | Cloud GPU Instances | Best For |
|---|---|---|---|
| Performance Consistency | Dedicated bare-metal — no noisy neighbor | Variable — shared infrastructure, noisy neighbor risk | On-Premises GPU Cluster |
| NVLink / NVSwitch Bandwidth | Full NVLink bandwidth within DGX nodes | Limited — most cloud instances lack NVSwitch | On-Premises GPU Cluster |
| Upfront Capital | High — $10M+ for 100-GPU cluster | Zero — pure OpEx | Cloud GPU Instances |
| Cost at Scale (3-year) | Lower TCO for sustained, high-utilization workloads | Higher — cloud GPU costs 3–5x on-prem at sustained use | On-Premises GPU Cluster |
| Data Sovereignty | Complete — data never leaves your facility | Depends on provider controls and region | On-Premises GPU Cluster |
| GPU Availability | Guaranteed once deployed | Constrained — H100 availability limited in all regions | On-Premises GPU Cluster |
| Flexibility / Elasticity | Fixed capacity — scale requires procurement lead time | Elastic — scale up or down in minutes | Cloud GPU Instances |
| Operational Burden | Full operational responsibility on your team | Managed infrastructure — lower ops burden | Cloud GPU Instances |
This comparison is a general framework. The right choice depends on your specific requirements, existing environment, and business objectives. DCS Global can help you evaluate the options for your situation.
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GPU Clusters — Frequently Asked Questions
Technical questions from AI engineers and IT architects evaluating GPU cluster deployments.