NVIDIA GeForce RTX 4080 SUPER vs NVIDIA GeForce RTX 3090 for AI
A head-to-head comparison of specs, pricing, and real-world AI performance to help you pick the right hardware.
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Quick Verdict
Both the NVIDIA GeForce RTX 4080 SUPER and NVIDIA GeForce RTX 3090 are strong contenders for AI workloads. Your choice should come down to specific workload requirements, budget, and ecosystem preferences. Check the specs comparison below to find the best fit.
NVIDIA GeForce RTX 4080 SUPER
$949 – $1,099
The sweet spot for AI on a budget. 16GB GDDR6X handles most 7B–13B parameter models for inference and fine-tuning, with excellent power efficiency under $1,100.
NVIDIA GeForce RTX 3090
$699 – $999
The best-value GPU for AI on a budget. 24GB VRAM at a fraction of the RTX 4090 price — runs most open-source LLMs and handles fine-tuning workloads well.
Specs Comparison
| Spec | NVIDIA GeForce RTX 4080 SUPER | NVIDIA GeForce RTX 3090 |
|---|---|---|
| Price | $949 – $1,099 | $699 – $999 |
| VRAM | 16GB GDDR6X | 24GB GDDR6X |
| CUDA Cores | 10,240 | 10,496 |
| Memory Bandwidth | 736 GB/s | 936 GB/s |
| TDP | 320W | 350W |
| Interface | PCIe 4.0 x16 | PCIe 4.0 x16 |
AI Benchmarks
Community-reported figures — see sources for methodology. Results may vary by system configuration.
| Benchmark | NVIDIA GeForce RTX 4080 SUPER | NVIDIA GeForce RTX 3090 |
|---|---|---|
| Llama 3 8B (Q4) | 52 tok/s | 48 tok/s |
| Stable Diffusion XL | 6.8 it/s | 5.8 it/s |
| Llama 3 70B (Q4) | — | 9 tok/s |
NVIDIA GeForce RTX 4080 SUPER
Pros
- +Strong price-to-performance for AI inference
- +Lower power draw than RTX 4090
- +Fits standard ATX cases easily
Cons
- -16GB VRAM limits larger model support
- -Not ideal for training large models
- -Previous-gen Ada Lovelace architecture
NVIDIA GeForce RTX 3090
Pros
- +Great price-to-performance ratio
- +24GB VRAM handles most models
- +Widely available on secondary market
Cons
- -Previous generation architecture
- -Higher power draw per FLOP vs 4090
- -No 4th-gen tensor cores
Where to Buy
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