RTX 5080 for AI: Fast, but 16GB Sets the Limit

The RTX 5080 specs that matter for local AI

The 5080 sits one step below the 5090 in NVIDIA's current lineup. For running local models, these are the numbers that decide what you get:

What fits on 16GB: the RTX 5080 model table

Same formula as the hardware calculator, run against 16 GB. "Fits" means under 90% of VRAM, leaving room for the driver and desktop. "Tight" means between 90% and just over 100%, where it may load or may spill into system RAM. Llama 3.3 70B is left out because it needs about 49 GB.

*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 pattern is clear: everything up to 14B fits at Q4, even with a long context in most cases, and 12B models fit at Q8 as a tight squeeze. The jump from 14B to 24B is where 16 GB runs out. Mistral Small 24B needs about 17 GB at Q4 with a short chat, just over the card's total.

If 16 GB covers the models you want to run, here's an affiliate link to current RTX 5080 listings on Amazon (opens in new tab). Compare it with the other 16GB cards first, since they all fit the same models.

RTX 5080 vs other 16GB cards

Every card in this table holds the same models, because they all have 16 GB. What changes is how fast those models run and what you pay.

The 5070 Ti is the one to compare closely. It has about 7% less memory bandwidth than the 5080, at a launch price $250 lower and 60 W less power. For running local models, where bandwidth drives generation speed and capacity is identical, that gap is small. The 5060 Ti 16GB has less than half the 5080's bandwidth, so the same models will generate noticeably slower, but they'll still fit. Launch prices are shown for scale only; check what each card actually sells for.

RTX 5080 vs a 24GB card

If the models you want are in the 24B to 32B range, a newer 16GB card won't get you there, however fast it is. An older 24GB card like the RTX 3090 runs Mistral Small 24B and Gemma 3 27B at Q4 with room to spare, while the 5080 can't load them at all. The trade is the opposite of what the model numbers suggest: the 5080 is newer, with more compute and similar memory bandwidth (960 GB/s vs 936 GB/s), while the 3090 holds bigger models. Decide which models you need first, then pick the card. The RTX 3090 vs RTX 4080 comparison shows the same trade-off with the previous 16GB flagship, model by model. The hardware guide lays out every tier side by side.

Context length on a 16GB card

Ollama's documentation lists a 4K default context for cards under 24 GiB, which includes the 5080. That keeps memory use low, but it also means long documents get cut off unless you raise it. If you do, check the long-context column above: Qwen 14B at Q4 goes from about 10.3 GB at 4K to about 15.6 GB at 32K, a tight fit on this card. Ollama's docs show how to set it, for example OLLAMA_CONTEXT_LENGTH=16384 ollama serve, and ollama ps shows whether the model still sits at 100% GPU.

Before you buy an RTX 5080

• Be sure 16 GB covers your models. If you want anything 24B or larger, this card isn't the right one, and no setting will change that.
• Compare it with the 5070 Ti at real prices. Both have 16 GB, and the bandwidth gap is small, so the price gap is what you're weighing.
• Plan the power. NVIDIA lists 850 W for the Founders Edition. Partner cards often run higher limits, so read the exact model's spec page.
• Check the case. The Founders Edition is 304 mm long and two slots thick, but NVIDIA's setup notes call for clearance for a three-slot card, and many partner cards are thicker.
• Test it before the return window closes. Load Qwen 14B at Q4, confirm ollama ps shows 100% GPU, and let it generate for a while as you watch temperatures.