NVIDIA GeForce RTX 4070 Ti Super vs AMD Radeon RX 7900 XTX
Detailed comparison of specifications, performance, and pricing between NVIDIA GeForce RTX 4070 Ti Super and AMD Radeon RX 7900 XTX
Difference Analysis
Full Specifications
| Specification | NVIDIA GeForce RTX 4070 Ti Super | AMD Radeon RX 7900 XTX | B200 | H100 PCIe |
|---|---|---|---|---|
| Brand | NVIDIA | AMD | NVIDIA | NVIDIA |
| Series | Consumer | Consumer | Data Center | Data Center |
| Architecture | Ada Lovelace | RDNA 3 | Blackwell | Hopper |
| VRAM | 16GB | 24GB | 192GB | 80GB |
| VRAM Type | GDDR6X | GDDR6 | HBM3e | HBM2e |
| Memory Bandwidth | 672 GB/s | 960 GB/s | 8.0 TB/s | 2.0 TB/s |
| FP16 TFLOPS | 88.2 | 122.0 | - | 102.0 |
| Tensor TFLOPS | 353.0 | - | 4.5k | 1.5k |
| TDP | 285W | 355W | 1000W | 350W |
| Form Factor | - | - | SXM | PCIe |
| Hardware Price | - | - | $$45k | $$28k |
| Cloud Price (min) | - | - | $3.75/hr | $2.89/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.
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NVIDIA GeForce RTX 4070 Ti Super vs AMD Radeon RX 7900 XTX FAQ
It depends on your use case. The NVIDIA GeForce RTX 4070 Ti Super offers 189% better performance (353.0 vs 122.0 TFLOPS). For raw performance, choose NVIDIA GeForce RTX 4070 Ti Super. For value, consider your budget and workload requirements.
The AMD Radeon RX 7900 XTX has more VRAM with 24GB compared to 16GB (50% more). More VRAM is crucial for training large models and running inference on bigger batch sizes.
For AI training, the AMD Radeon RX 7900 XTX is generally better due to its larger VRAM (24GB). Large language models and deep learning workloads benefit significantly from more memory. However, if your models fit in 16GB, 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 NVIDIA GeForce RTX 4070 Ti Super would give you 189% more performance and similar VRAM. Consider if your workloads are bottlenecked by current GPU capabilities.
