NVIDIA GeForce RTX 5080 vs NVIDIA GeForce RTX 4090 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 are excellent choices for AI. The NVIDIA GeForce RTX 5080 comes in at a lower price and offers strong performance. The NVIDIA GeForce RTX 4090 justifies its premium with higher-end specs. Choose based on your budget and whether you need the extra headroom.
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 4090
$1,599 – $1,999
The best consumer GPU for AI. 24GB GDDR6X with 16,384 CUDA cores handles 70B+ parameter models locally — the go-to choice for serious AI workstations and local LLM setups.
Specs Comparison
| Spec | NVIDIA GeForce RTX 5080 | NVIDIA GeForce RTX 4090 |
|---|---|---|
| Price | $999 – $1,099 | $1,599 – $1,999 |
| VRAM | 16GB GDDR7 | 24GB GDDR6X |
| CUDA Cores | 10,752 | 16,384 |
| Memory Bandwidth | 960 GB/s | 1,008 GB/s |
| TDP | 360W | 450W |
| 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 4090 |
|---|---|---|
| Llama 3 8B (Q4) | 72 tok/s | 62 tok/s |
| Stable Diffusion XL | 10.1 it/s | 8.2 it/s |
| Llama 3 70B (Q4) | — | 12 tok/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 4090
Pros
- +Proven workhorse for AI inference
- +Excellent VRAM capacity for most models
- +Strong community support and documentation
Cons
- -High power consumption
- -Premium pricing
- -Previous-gen Ada Lovelace architecture
Where to Buy
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