# Can I run Gemma 4 31B on an RTX 3090? | What Can I Run AI?

> Yes, but it is tight. Gemma 4 31B at Q4_K_M needs about 22.8 GB; RTX 3090 has 24.0 GB usable. Estimated tokens/sec and context headroom.

Source: https://whatcanirunai.vercel.app/can-i-run/gemma-4-31b-it/rtx-3090/

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**Yes, but it is tight.** Gemma 4 31B at Q4\_K\_M needs about 22.8 GB, and the GeForce RTX 3090 has 24.0 GB usable at a \~4k context.

# Can I run Gemma 4 31B on an RTX 3090?

Gemma 4 31B at Q4\_K\_M needs about 22.8 GB, and the GeForce RTX 3090 has 24.0 GB usable. That figure is 24 GB of VRAM on the spec sheet, of which 24.0 GB is usable for weights and cache, at 936 GB/s memory bandwidth. Google’s dense Gemma 4 31B. Close in memory to Qwen3-32B, with a different training mix. These are estimates from [our formula](https://whatcanirunai.vercel.app/method/), not measured benchmarks.

Quant that fits

Q4\_K\_M Fits, short context only

Memory needed

22.8 GB at Q4\_K\_M, \~4k context

Usable VRAM

24.0 GB of 24 GB VRAM

Est. tokens/sec

30–42 estimate

[Gemma 4 31B](https://whatcanirunai.vercel.app/models/gemma-4-31b-it/) · [GeForce RTX 3090](https://whatcanirunai.vercel.app/hardware/rtx-3090/) · [Everything this machine runs](https://whatcanirunai.vercel.app/what-can-i-run/rtx-3090/) · [Q4 vs Q8 vs FP16](https://whatcanirunai.vercel.app/quantization/)

## Memory at each quant

| Quant    | Need vs 24.0 GB | Verdict       |
| -------- | --------------- | ------------- |
| Q4\_K\_M | 22.8 GB         | Q4Tight       |
| Q8\_0    | 39.2 GB         | Q8Won’t fit   |
| FP16     | 72.9 GB         | FP16Won’t fit |

## Estimated tokens per second

Estimate: **30–42 tokens/sec** at Q4\_K\_M. This is a bandwidth-derived range, not a bench. Efficiency is a 60–85% GPU band from [How we calculate](https://whatcanirunai.vercel.app/method/).

## Context headroom

We do not have a published KV config for this model, so the memory need is the \~4k-context estimate only. Use the [VRAM calculator](https://whatcanirunai.vercel.app/vram-calculator/) to explore longer chats.

## What to do instead

* Run [Qwen3-30B-A3B](https://whatcanirunai.vercel.app/can-i-run/qwen3-30b-a3b/rtx-3090/) instead (22.3 GB at Q4). It is a different model, not a drop-in, but it does fit this machine.
* Q8\_0 needs 39.2 GB and does not fit here. The [Mac mini M5 Pro 64GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m5-pro-64/) has 48.0 GB usable, enough for Q8.
* See the [best GPU for this model family](https://whatcanirunai.vercel.app/best-gpu-for/gemma-4/) and the [Gemma 4 31B model page](https://whatcanirunai.vercel.app/models/gemma-4-31b-it/).

## Other hardware for Gemma 4 31B

* [Gemma 4 31B on an RTX 3060 12GB](https://whatcanirunai.vercel.app/can-i-run/gemma-4-31b-it/rtx-3060-12gb/)Won’t fit
* [Gemma 4 31B on an RTX 3070 Ti](https://whatcanirunai.vercel.app/can-i-run/gemma-4-31b-it/rtx-3070-ti/)Won’t fit
* [Gemma 4 31B on an RTX 3080](https://whatcanirunai.vercel.app/can-i-run/gemma-4-31b-it/rtx-3080-10gb/)Won’t fit
* [Gemma 4 31B on an RTX 4060 Ti 16GB](https://whatcanirunai.vercel.app/can-i-run/gemma-4-31b-it/rtx-4060-ti-16gb/)Won’t fit
* [Gemma 4 31B on an RTX 4070 Ti Super](https://whatcanirunai.vercel.app/can-i-run/gemma-4-31b-it/rtx-4070-ti-super/)Won’t fit
* [Gemma 4 31B on an RTX 4090](https://whatcanirunai.vercel.app/can-i-run/gemma-4-31b-it/rtx-4090/)Tight

## Other models on an RTX 3090

* [Qwen3-32B on an RTX 3090](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/rtx-3090/)Tight
* [Qwen3-30B-A3B on an RTX 3090](https://whatcanirunai.vercel.app/can-i-run/qwen3-30b-a3b/rtx-3090/)Tight
* [Qwen3.8-27B on an RTX 3090](https://whatcanirunai.vercel.app/can-i-run/qwen3.8-27b/rtx-3090/)Fits
* [Gemma 3 27B on an RTX 3090](https://whatcanirunai.vercel.app/can-i-run/gemma-3-27b-it/rtx-3090/)Fits
* [gpt-oss-20b on an RTX 3090](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-20b/rtx-3090/)Fits
* [Mistral Small 24B on an RTX 3090](https://whatcanirunai.vercel.app/can-i-run/mistral-small-3.2-24b-instruct-2506/rtx-3090/)Fits

## Quick answers

Can I run Gemma 4 31B on an RTX 3090?

Yes, but it is tight. Gemma 4 31B at Q4\_K\_M needs about 22.8 GB, and the GeForce RTX 3090 has 24.0 GB usable.

How much VRAM does Gemma 4 31B need?

About 22.8 GB at Q4\_K\_M, 39.2 GB at Q8\_0 and 72.9 GB at FP16, estimated for a \~4k context.

How fast is Gemma 4 31B on an RTX 3090?

Our estimate is 30–42 tokens per second at Q4\_K\_M. It is not a measured benchmark.

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