NVIDIA GeForce RTX 5080 vs NVIDIA GeForce RTX 4080 SUPER 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
The NVIDIA GeForce RTX 5080 is the better performer but costs more. Choose it if you need top-tier AI performance and can justify the price premium. The NVIDIA GeForce RTX 4080 SUPER delivers solid value at a lower price point and is the smarter pick for budget-conscious buyers.
NVIDIA GeForce RTX 5080
$999 – $1,099
The Blackwell mid-range powerhouse for local AI. 16GB GDDR7 with 10,752 CUDA cores delivers strong performance for 13B–30B parameter models at a significantly lower price than the RTX 5090 — the best price/performance option for most AI workloads in 2026.
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.
Specs Comparison
| Spec | NVIDIA GeForce RTX 5080 | NVIDIA GeForce RTX 4080 SUPER |
|---|---|---|
| Price | $999 – $1,099 | $949 – $1,099 |
| VRAM | 16GB GDDR7 | 16GB GDDR6X |
| CUDA Cores | 10,752 | 10,240 |
| Memory Bandwidth | 960 GB/s | 736 GB/s |
| TDP | 360W | 320W |
| Interface | PCIe 5.0 x16 | PCIe 4.0 x16 |
AI Benchmarks
Community-reported figures — see sources for methodology. Results may vary by system configuration.
| Benchmark | NVIDIA GeForce RTX 5080 | NVIDIA GeForce RTX 4080 SUPER |
|---|---|---|
| Llama 3 8B (Q4) | 72 tok/s | 52 tok/s |
| Stable Diffusion XL | 10.1 it/s | 6.8 it/s |
NVIDIA GeForce RTX 5080
Pros
- +Strong 16GB GDDR7 for 13B–30B parameter models
- +Blackwell architecture with 5th-gen tensor cores
- +More affordable than RTX 5090 with great price/performance
- +Good power efficiency vs performance
Cons
- -Only 16GB VRAM — can't run 70B models without heavy quantization
- -Hard to find at MSRP
- -No VRAM upgrade path
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
Where to Buy
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