GeForce RTX 5080 vs GeForce RTX 4070 Ti SUPER for Local AI
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Which GPU is better for local AI workloads?
Both cards have 16GB of GDDR7-class memory capacity and run 8B–14B LLMs comfortably. The RTX 5080 delivers 960 GB/s memory bandwidth versus 672 GB/s on the 4070 Ti SUPER, which translates directly into faster LLM token generation. The 4070 Ti SUPER draws 285W versus 360W, so it fits smaller power budgets.
Spec comparison: what actually differs
The table below is computed live from our hardware database. Positive deltas favor the GeForce RTX 5080.
| Specification | GeForce RTX 5080 | GeForce RTX 4070 Ti SUPER | Difference |
|---|---|---|---|
| VRAM | 16 | 16 | — |
| Memory bandwidth | 960 | 672 | +43% |
| Memory type | GDDR7 | GDDR6X | — |
| Memory bus | 256 | 256 | — |
| TDP | 360 | 285 | +26% |
| CUDA cores | 10752 | 8448 | +27% |
| Architecture | Blackwell | Ada Lovelace | — |
| Launch MSRP | $999 | $799 | +25% |
| Street price | $999 | $799 | +25% |
What about price?
Which one should you buy for LLMs and image generation?
GeForce RTX 5080
Buy the RTX 5080 if you generate images or run LLMs daily and want the fastest 16GB card of the two.
Full specs & benchmarks →GeForce RTX 4070 Ti SUPER
Buy the RTX 4070 Ti SUPER if you can find it well under its $799 MSRP, or your PSU tops out near 300W.
Full specs & benchmarks →Is it faster for LLM inference?
For local LLM inference the 5080 is faster: its 960 GB/s bandwidth moves tokens noticeably quicker than the 4070 Ti SUPER's 672 GB/s at the same 16GB capacity, so model selection is identical but decode speed is not.
How does it handle image generation?
For Stable Diffusion and Flux both cards hold the same model set in 16GB; the 5080's extra bandwidth and newer Blackwell tensor cores push out images roughly 30% faster in comparable tests.
Which AI models fit on each card?
Computed from our model VRAM database at Q4 quantization: GeForce RTX 5080 has 16 GB, GeForce RTX 4070 Ti SUPER has 16 GB.
Fit on both cards
Frequently asked questions
Is the RTX 5080 worth $200 more than the 4070 Ti SUPER for AI?
Yes for daily AI workloads: you get 960 GB/s versus 672 GB/s memory bandwidth at identical 16GB capacity, which speeds up every LLM and image workload. No if you run models only occasionally — capacity, not speed, decides which models load.
Do both cards run the same AI models?
Yes. Both have 16GB VRAM, so both fit 14B-class LLMs at Q8, 8B models with large context, and Flux with offloading tricks. The 5080 just runs them faster.
Which card is more power-efficient for AI?
The 4070 Ti SUPER draws 285W versus the 5080's 360W. Per token generated the cards are close because the 5080 finishes the same work faster.