RTX A6000 vs RTX 5090 for AI Training: Which GPU Should You Rent?
Choosing between RTX A6000 and RTX 5090 for your AI training workloads? We've benchmarked both GPUs across popular ML frameworks to help you make the right decision.
Executive Summary
Choose RTX A6000 if:
- • You need maximum VRAM (48GB)
- • Working with large language models
- • Budget-conscious (€0.89/hr)
- • Stable, proven architecture
- • Professional workstation features
Choose RTX 5090 if:
- • You prioritize raw performance
- • Working with computer vision
- • Need latest architecture benefits
- • Faster training is worth extra cost
- • Mixed AI/rendering workloads
Technical Specifications Comparison
| Specification | RTX A6000 | RTX 5090 | Winner |
|---|---|---|---|
| VRAM | 48GB GDDR6 | 32GB GDDR7 | RTX A6000 |
| CUDA Cores | 10,752 | 21,760 | RTX 5090 |
| Memory Bandwidth | 768 GB/s | 1,792 GB/s | RTX 5090 |
| Tensor Performance | 309 TOPS | 756 TOPS | RTX 5090 |
| Power Consumption | 300W | 575W | RTX A6000 |
| Rental Price | €0.89/hr | €1.49/hr | RTX A6000 |
AI Training Benchmarks
We tested both GPUs across popular AI frameworks and model types. All tests used identical software configurations and datasets.
*Out of Memory due to 32GB VRAM limit
Key Findings:
- • RTX 5090 is 30-45% faster for most training workloads
- • RTX A6000 handles larger models due to 48GB VRAM
- • Both GPUs show excellent mixed precision performance
- • Memory bandwidth advantage gives RTX 5090 edge in data-intensive tasks
Cost-Performance Analysis
While RTX 5090 offers superior performance, the RTX A6000 provides better value for many use cases.
RTX 5090 costs 15% more but finishes 31% faster
RTX A6000 offers 15% better value
Use Case Recommendations
- • Large Language Models: 48GB VRAM handles bigger models
- • Budget-Conscious Projects: 40% lower rental cost
- • Long Training Jobs: Better cost efficiency over time
- • Research & Experimentation: More VRAM for model exploration
- • Multi-Model Training: Run multiple models simultaneously
- • Computer Vision: Superior performance for image processing
- • Time-Critical Projects: 30-45% faster training
- • Production Workloads: Latest architecture and features
- • Mixed Workloads: Excellent for AI + rendering
- • Inference Deployment: Better throughput for serving models
Memory Usage Patterns
Understanding VRAM requirements is crucial for choosing the right GPU for your specific models.
| Model | Parameters | Training VRAM | A6000 Fit? | 5090 Fit? |
|---|---|---|---|---|
| BERT-Base | 110M | ~4GB | ✓ | ✓ |
| GPT-2 (1.5B) | 1.5B | ~12GB | ✓ | ✓ |
| LLaMA-7B | 7B | ~28GB | ✓ | ✓ |
| LLaMA-13B | 13B | ~42GB | ✓ | ✗ |
| LLaMA-30B | 30B | ~60GB | ✗* | ✗ |
*Requires gradient checkpointing and optimization techniques
Final Recommendation
For most AI researchers and developers, we recommend starting with the RTX A6000. The combination of 48GB VRAM, excellent performance, and lower cost (€0.89/hr) makes it the best value proposition for AI training workloads.
Choose RTX 5090 only if you specifically need the extra performance for time-critical projects or are working primarily with computer vision models that benefit from the higher memory bandwidth.
Quick Decision Matrix:
- • Budget < €1/hour: RTX A6000
- • Model > 10B parameters: RTX A6000
- • Computer vision focus: RTX 5090
- • Time is critical: RTX 5090
- • Research/experimentation: RTX A6000
Ready to Start Training?
Both GPUs are available for immediate deployment. Start with RTX A6000 and upgrade if needed.