GeForce RTX 3090 vs GeForce RTX 4080 SUPER for Local AI
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Which GPU is better for local AI workloads?
The 3090's 24GB holds 32B models at Q4 — beyond the 4080 SUPER's 16GB — and its 936 GB/s actually beats the 4080 SUPER's 736. The 4080 SUPER counters with far better compute efficiency, newer features, and warranty when bought new. Capacity versus polish.
Spec comparison: what actually differs
The table below is computed live from our hardware database. Positive deltas favor the GeForce RTX 3090.
| Specification | GeForce RTX 3090 | GeForce RTX 4080 SUPER | Difference |
|---|---|---|---|
| VRAM | 24 | 16 | +50% |
| Memory bandwidth | 936 | 736 | +27% |
| Memory type | GDDR6X | GDDR6X | — |
| Memory bus | 384 | 256 | — |
| TDP | 350 | 320 | +9% |
| CUDA cores | 10496 | 10240 | +3% |
| Architecture | Ampere | Ada Lovelace | — |
| Launch MSRP | $1,499 | $999 | +50% |
| Street price | $1,499 | $999 | +50% |
What about price?
Which one should you buy for LLMs and image generation?
GeForce RTX 3090
Buy the used 3090 for maximum model size per dollar.
Full specs & benchmarks →GeForce RTX 4080 SUPER
Buy the 4080 SUPER for efficiency, warranty, and newer stack.
Full specs & benchmarks →Is it faster for LLM inference?
32B Q4 only on 3090; 14B-class models decode faster on the 3090's 936 GB/s than the 4080 SUPER's 736.
How does it handle image generation?
4080 SUPER faster per image on compute; 3090 batches larger.
Which AI models fit on each card?
Computed from our model VRAM database at Q4 quantization: GeForce RTX 3090 has 24 GB, GeForce RTX 4080 SUPER has 16 GB.
Fit only on the GeForce RTX 3090 (24 GB)
Fit on both cards
Frequently asked questions
3090 or 4080 SUPER for 32B models?
3090 — 32B at Q4 needs ~19GB, beyond the 4080 SUPER's 16GB.
Which decodes 14B models faster?
The 3090, on bandwidth: 936 versus 736 GB/s. Compute-heavy prompt processing favors the 4080 SUPER.