Introduction
The choice of GPU hardware for any data center involves a careful juxtaposition of performance requirements, budget constraints, and operational aspects. It’s a complete, systematic study of existing options on the market, including in-depth comparisons to help you choose what will work best for your GPU fabrication.
Enterprise GPU Solutions
NVIDIA Data Center GPUs
NVIDIA A100
The A100 is NVIDIA’s flagship data center GPU and delivers:
- Performance: Up to 624 teraflops
- Memory: 40GB/80GB options
- Bandwidth: 1,555 GB/s
- Signature Feature: Multi-instance GPU (MIG) technology
- Best Fit: Enterprise AI/HPC workloads
The MIG in the A100 supports up to 7 isolated GPU instances, providing unparalleled flexibility in resource allocation and workload management.
NVIDIA V100
The V100 remains a dominant selection for many groups:
- Performance: Up to 149 teraflops (yes, that’s teraflops, 16th of a petaflops)
- Memory: 32GB
- Memory Bus: 4,096-bit
- Best Used For: Big Data / Deep Learning and HPC Applications
NVIDIA P100
Optimal for some workloads at a better price point:
- Performance: Up to 21 teraflops
- Memory: 16GB
- Memory Bus: 4,096-bit
- Important Feature: Pascal architecture
- Best Usage Example: Entry level HPC & ML workloads
Alternative Solutions
Google’s TPU
Google’s custom ASICs have some benefits that are not found in their market competitors:
- Performance: Up to 420 teraflops
- Memory: 128GB HBM
- Important Function: TensorFlow optimization
- Limitations: Only available on cloud
- Use Case: Data Streams Based on TensorFlow Workflows
Server Configurations
NVIDIA DGX Systems
DGX A100
NVIDIA’s new AI system training technology offers:
- Computing Power: 5 petaflops
- RAM Options: 320GB or 640GB
- Data: 8x 200Gb/s HDR InfiniBand
- Storage: 15TB NVMe SSD
- Cost: Premium enterprise
- Use Case: Large-scale artificial intelligence research
DGX Station A100
A desktop-grade solution offering:
- GPU Configurations: 160GB or 320GB
- Form Factor: Desktop workstation
- Cooling: High-performance and scalable liquid cooling
- Target usage: Dev teams and rather small departments
- Plus: Requirement of no data center
DGX SuperPOD
Enterprise-scale solution delivery:
- Scale: Up to 140 DGX A100 systems
- Computing Power: 700 petaflops
- Orientation: End-to-end infrastructure
- Management: NVIDIA Base Command
- Use case: Large enterprise AI infrastructure
Custom Build Options
Lambda Labs Workstations
Solutions you can have at a mid-range price:
- Number of GPUs: 2–4 GPUs per system
- Ideal User: Individual researchers and small teams
- Cost: Less than DGX options
- Flexibility: Configurations that can be configured
- Support: Not as robust as enterprise solutions
Performance Comparisons
Deep Learning Performance
PyTorch Performance Metrics for Popular Deep Learning Tasks
Image Classification (ResNet-50)
- DGX A100: 2,400 images/second
- DGX-2: 1,800 images/second
- Custom 4x V100: 1,400 images/sec
NLP Training (BERT-Large)
- DGX A100: V100-based systems are 3x faster
- TPUv3: Like an A100, but for TensorFlow
- Custom Solutions: Performance depends on the configuration
Cost-Performance Analysis
Cost per teraflops comparison:
- A100-based Systems: Highest starting cost, highest performance
- V100-based Systems: Middle-of-the-road cost, top performance
- Customs: Less price up-front. Performance may vary
- Cloud Solutions: No upfront costs, but larger ongoing fees
Total Cost of Ownership
Direct Costs
- Hardware acquisition
- Infrastructure modifications
- Cooling systems
- Power supply units
- Networking equipment
Operational Costs
- Power consumption
- Cooling expenses
- Maintenance
- Support contracts
- Software licenses
Hidden Costs
- Training requirements
- Integration expenses
- Downtime costs
- Upgrade paths
- End-of-life considerations
Management Solutions
Run: AI Platform
Complete management solution offering:
- Resource pooling
- Dynamic allocation
- Quota management
- Visibility tools
- Automated scheduling
Other Management Strategies
- Kubernetes with GPU operators
- SLURM workload manager
- No code, custom orchestration solutions
- Vendor-specific tools
Decision Framework
Evaluation Criteria
When choosing hardware, keep these factors in mind:
Workload Requirements
- Training vs. inference
- Model size and complexity
- Batch size requirements
- Memory demands
Operational Factors
- Power availability
- Cooling capacity
- Space constraints
- Network infrastructure
Business Considerations
- Budget constraints
- Growth projections
- ROI requirements
- Support needs
Recommended Configurations
Teams with Less than Five Data Scientists
- Tailored workstations or systems with limited DGX Station deployment
- 2–4 GPUs per system
- Local management tools
- Direct-attached storage
Medium Organizations (5–20 Data Scientists)
- Mix of DGX and custom systems
- Centralized storage
- Basic orchestration
- Maintenance contracts
Enterprise Scale (20+ Data Scientists)
- DGX SuperPOD or equivalent
- Full infrastructure stack
- Advanced orchestration
- Enterprise support and services
Conclusion
Balancing your performance needs, budget, and operational requirements will help determine the most efficient GPU hardware for your data center. This guide helps you make informed decisions depending on your organization’s specific needs and context.