AI Research Cluster
High-density GPU cluster for deep learning research, model training, and scientific computing. This reference architecture delivers 40+ GPUs with petabyte-scale high-performance storage and 400Gbps InfiniBand fabric—designed for universities, research labs, and AI centers of excellence.
Executive Summary
Production-grade AI infrastructure for large-scale model training and research workloads.
Use Case
Large Language Model training, computer vision research, scientific simulation, drug discovery, and multi-GPU distributed training workloads.
Challenges Addressed
GPU utilization optimization, storage I/O bottlenecks, multi-node synchronization, power density, and cooling for high-wattage GPUs.
Key Outcomes
Linear scaling for distributed training, 90%+ GPU utilization, shared research datasets, and job scheduling for multi-tenant access.
Architecture Overview
Total GPU Compute Summary
Detailed Bill of Quantities
Complete hardware specification for 40-GPU research cluster.
GPU Compute Nodes
5 Units| Component | Specifications | Qty | Purpose |
|---|---|---|---|
|
8-GPU Training Server
RDP-GPU-8U-H100
|
|
5× | Distributed training nodes (40 GPUs total) |
High-Performance Storage
1 PB Cluster| Component | Specifications | Qty | Purpose |
|---|---|---|---|
|
All-Flash Scratch Storage
RDP-PFS-NVMe-200T
|
|
2× | Training scratch, checkpoints (~400TB flash) |
|
Capacity Storage Array
RDP-NAS-4U-60B
|
|
2× | Dataset storage, model archive (~1PB) |
|
Parallel File System Metadata
RDP-MDS-2U-NVMe
|
|
2× | HA metadata servers for PFS |
Data Preprocessing Servers
4 Units| Component | Specifications | Qty | Purpose |
|---|---|---|---|
|
CPU Compute Server
RDP-SRV-2U-HPC
|
|
4× | Data preprocessing, ETL, feature engineering |
High-Speed Interconnect
InfiniBand NDR| Component | Specifications | Qty | Purpose |
|---|---|---|---|
|
InfiniBand NDR Switch
IB-SW-NDR-64
|
|
2× | GPU fabric spine switches |
|
Ethernet Management Switch
SW-MGMT-48X100G
|
|
2× | Management & storage Ethernet |
|
Optical Cabling Kit
OPTICS-NDR-KIT
|
|
1× Kit | Cluster interconnect cabling |
Power & Cooling Requirements
Critical infrastructure planning for high-density GPU deployment.
Power Budget
| Component | Qty | Max Power | Typical |
|---|---|---|---|
| GPU Servers (8U) | 5 | 51 kW | 45 kW |
| Storage Nodes | 6 | 6 kW | 4.5 kW |
| CPU Servers | 4 | 3.2 kW | 2.4 kW |
| Network Switches | 4 | 2.4 kW | 1.8 kW |
| Total | — | ~63 kW | ~54 kW |
Cooling Requirements
- Cooling Capacity ~220,000 BTU/hr
- Recommended Cooling Direct Liquid Cooling (DLC)
- Alternative Rear-door Heat Exchanger
- Inlet Temperature 18°C – 27°C
- Airflow ~15,000 CFM
- Rack Density 30-40 kW per rack
Electrical Infrastructure
- Input Power 3-Phase 415V AC
- Circuit Capacity 2× 100A 3-Phase
- UPS Capacity 80 kVA (minimum)
- Generator Backup Required (100 kVA+)
- PDU per Rack 2× 60A 3-Phase
Physical Space
- Rack Count 2-3 Full Racks
- Floor Space ~6 m² (rack footprint)
- Weight per Rack ~1,200 kg
- Floor Load Reinforced required
- Ceiling Height 3m+ recommended
Software Stack
Operating System
- Ubuntu 22.04 LTS (HPC optimized)
- RHEL 9.x / Rocky Linux 9
- NVIDIA GPU Driver 535+
- CUDA 12.x Toolkit
AI/ML Frameworks
- PyTorch 2.x + DeepSpeed
- TensorFlow 2.x + Horovod
- NVIDIA NeMo, Megatron-LM
- JAX, Hugging Face Transformers
Cluster Management
- Slurm Workload Manager
- Kubernetes + NVIDIA GPU Operator
- Lustre / GPFS / BeeGFS
- Prometheus + Grafana Monitoring
Reference Architecture Disclaimer
This reference architecture is provided for planning and discussion purposes. GPU availability subject to NVIDIA allocation and lead times. Actual configurations may vary based on specific research workloads, facility capabilities, and budget. Liquid cooling infrastructure may require additional site preparation. Final BOQ will be prepared after detailed requirements analysis and site assessment.
RDP Hardware Portfolio
Make in India certified infrastructure manufactured at our Hyderabad facility.
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