RTX 3090 vs RTX 4080 for Local AI: Older Card, More Memory

RTX 3090 vs RTX 4080: the specs side by side

CUDA core counts don't compare directly across architectures, since clock speeds and per-core design differ, so don't read the 3090's slightly higher count as more compute.

Where the fit differs: 24GB vs 16GB

We ran 7 models across five quantization and context settings through the hardware calculator's formula on both cards. These are the 12 combinations where they give different answers:

Every row goes the 3090's way. The headline cases are Mistral Small 24B and Gemma 3 27B at Q4, which fit comfortably on the 3090 and don't load on the 4080 at all, and 14B models at Q8, which fit on the 3090 only. Everything at 12B and below at Q4 fits on both. The full per-card tables are on the RTX 3090 and RTX 5080 pages; the 5080 has the same 16 GB as the 4080, so its table applies here too.

*Gemma 3 figures are on the cautious side. Gemma 3's published config keeps only a 1,024-token window for five of every six layers, but the calculator counts every layer at full context. Runtimes that store just that window for those layers need less memory than shown, especially at 32K.

The bandwidth surprise

Most people expect the newer card to win everywhere. For text generation, it doesn't. Generation speed is mostly limited by memory bandwidth, and the 3090's wider 384-bit bus gives it about 31% more than the 4080 (936 vs about 717 GB/s). On a model both cards can fit, that favors the 3090 once the answer starts streaming.

The 4080 earns its place on compute. Its newer architecture is stronger at work that leans on the GPU cores rather than memory, like processing a long prompt before the first word comes out, or gaming and image generation. We haven't benchmarked either card, so this page sticks to what the published specs support.

The RTX 4080 Super doesn't change the picture. NVIDIA lists it with the same 16 GB and 256-bit bus, so every fit verdict above applies to it as well.

Context defaults differ too

Ollama sets its default context length from your VRAM. Its documentation lists 4K under 24 GiB and 32K from 24 to 48 GiB. On a 4080, that means a short 4K default, which keeps memory use low but cuts off long documents unless you raise it. On a 3090, the default is 32K, which is more useful for long inputs but pushes 32B models past the card. Either way, ollama ps shows whether the model is running at 100% GPU, and the 32K context catch explains how to set it.

Power and running cost

This is one area where the 4080 comes out ahead. It's rated 30 W lower, and NVIDIA recommends the same 750 W power supply for both. The 4080 uses the 16-pin connector with a three 8-pin adapter; the 3090 Founders Edition uses a 12-pin plug with a two 8-pin adapter.

The calculator uses each card's rated power, so treat the results as an upper limit. A card generating text often draws less than its rating, and an idle card draws far less.

Which one to buy for local AI

Pick the RTX 3090 if you want to run 24B to 32B models, Q8 on 14B models, or longer contexts on mid-size models. Its bandwidth lead should also favor it when generating text on models both can fit, and it's the only one of the two that supports NVLink. Its used-buying checklist is worth reading first, since most 3090s for sale are used.

Pick the RTX 4080 if the models you run are 14B or smaller, you also game on the card, or you'd rather buy a newer card with a slightly lower power rating. If those 16 GB are all you need, the RTX 5080 page compares the current 16GB cards.