RTX 4090 vs RTX 4080
Detailed comparison of specifications, performance, and pricing between NVIDIA GeForce RTX 4090 and NVIDIA GeForce RTX 4080
Difference Analysis
Full Specifications
| Specification | RTX 4090 | RTX 4080 |
|---|---|---|
| Brand | NVIDIA | NVIDIA |
| Series | Consumer | Consumer |
| Architecture | Ada Lovelace | Ada Lovelace |
| VRAM | 24GB | 16GB |
| VRAM Type | GDDR6X | GDDR6X |
| Memory Bandwidth | 1.0 TB/s | 717 GB/s |
| FP16 TFLOPS | 165.2 | 97.5 |
| Tensor TFLOPS | - | - |
| TDP | 450W | 320W |
| Form Factor | PCIe | PCIe |
| Hardware Price | $$1.8k | $$900 |
| Cloud Price (min) | $0.235/hr | $0.500/hr |
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RTX 4090 vs RTX 4080 FAQ
It depends on your use case. The RTX 4090 offers 69% better performance (165.2 vs 97.5 TFLOPS). However, the RTX 4080 is 100% cheaper. For raw performance, choose RTX 4090. For value, consider your budget and workload requirements.
The RTX 4090 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 RTX 4090 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.
The RTX 4080 is 100% cheaper at $$900 vs $$1.8k. When considering performance per dollar, evaluate your specific workload requirements to determine the best value.
Upgrading to RTX 4090 would give you 69% more performance and 50% more VRAM. The upgrade cost difference is approximately $$900. Consider if your workloads are bottlenecked by current GPU capabilities.