Tesla V100 vs AMD Radeon RX 7900 XTX
Detailed comparison of specifications, performance, and pricing between Tesla V100 and AMD Radeon RX 7900 XTX
Difference Analysis
Full Specifications
| Specification | Tesla V100 | AMD Radeon RX 7900 XTX | H100 | B200 |
|---|---|---|---|---|
| Brand | NVIDIA | AMD | NVIDIA | NVIDIA |
| Series | Data Center | Consumer | Data Center | Data Center |
| Architecture | Volta | RDNA 3 | Hopper | Blackwell |
| VRAM | 32GB | 24GB | 80GB | 192GB |
| VRAM Type | HBM2 | GDDR6 | HBM3 | HBM3e |
| Memory Bandwidth | 900 GB/s | 960 GB/s | 3.4 TB/s | 8.0 TB/s |
| FP16 TFLOPS | 31.4 | 122.0 | 134.0 | - |
| Tensor TFLOPS | 125.0 | - | 2.0k | 4.5k |
| TDP | 300W | 355W | 700W | 1000W |
| Form Factor | SXM | - | SXM | SXM |
| Hardware Price | $$2.5k | - | $$32k | $$45k |
| Cloud Price (min) | $0.140/hr | - | $1.47/hr | $3.75/hr |
Which Should You Choose?
For AI Training
Large model training needs maximum VRAM and memory bandwidth.
For AI Inference
Inference prioritizes throughput and cost efficiency.
Related Comparisons
Tesla V100 vs AMD Radeon RX 7900 XTX FAQ
It depends on your use case. The Tesla V100 offers 2% better performance (125.0 vs 122.0 TFLOPS). For raw performance, choose Tesla V100. For value, consider your budget and workload requirements.
The Tesla V100 has more VRAM with 32GB compared to 24GB (33% more). More VRAM is crucial for training large models and running inference on bigger batch sizes.
For AI training, the Tesla V100 is generally better due to its larger VRAM (32GB). Large language models and deep learning workloads benefit significantly from more memory. However, if your models fit in 24GB, the cheaper option may be more cost-effective.
Price comparison requires both GPUs to have available pricing data. Check individual GPU pages for current market prices.
Upgrading to Tesla V100 would give you 2% more performance and 33% more VRAM. Consider if your workloads are bottlenecked by current GPU capabilities.
