# Can I run Qwen3-32B on an RTX 3090? | What Can I Run AI?

> Yes, but it is tight. Qwen3-32B at Q4_K_M needs about 23.8 GB; RTX 3090 has 24.0 GB usable. Estimated tokens/sec and context headroom.

Source: https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/rtx-3090/

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**Yes, but it is tight.** Qwen3-32B at Q4\_K\_M needs about 23.8 GB, and the GeForce RTX 3090 has 24.0 GB usable at a \~4k context.

# Can I run Qwen3-32B on an RTX 3090?

Qwen3-32B at Q4\_K\_M needs about 23.8 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. Alibaba’s dense 32B Qwen3, a common “best quality that still fits a 24 GB card” pick. 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

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

Usable VRAM

24.0 GB of 24 GB VRAM

Est. tokens/sec

28–40 estimate

[Qwen3-32B](https://whatcanirunai.vercel.app/models/qwen3-32b/) · [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 | 23.8 GB         | Q4Tight       |
| Q8\_0    | 41.0 GB         | Q8Won’t fit   |
| FP16     | 76.4 GB         | FP16Won’t fit |

## Estimated tokens per second

Estimate: **28–40 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

At Q4\_K\_M, this machine has headroom out to about **4k tokens** under our KV estimate. Longer chats need more memory for the cache. Figures assume an FP16 KV cache.

__Context headroom for Qwen3-32B on an RTX 3090 at Q4\_K\_M__
| Context | KV cache (FP16) | Total at Q4\_K\_M | Verdict   |
| ------- | --------------- | ----------------- | --------- |
| 4k      | 1.1 GB          | 23.8 GB           | Tight     |
| 8k      | 2.1 GB          | 24.9 GB           | Won’t fit |
| 16k     | 4.3 GB          | 27.1 GB           | Won’t fit |
| 32k     | 8.6 GB          | 31.4 GB           | Won’t fit |
| 64k     | 17.2 GB         | 39.9 GB           | Won’t fit |
| 128k    | 34.4 GB         | 57.1 GB           | Won’t fit |

From the model’s config. Try other sizes in the [VRAM calculator](https://whatcanirunai.vercel.app/vram-calculator/) or see the [context length guide](https://whatcanirunai.vercel.app/context-length/).

## What to do instead

* Run [Gemma 4 31B](https://whatcanirunai.vercel.app/can-i-run/gemma-4-31b-it/rtx-3090/) instead (22.8 GB at Q4). It is a different model, not a drop-in, but it does fit this machine.
* Q8\_0 needs 41.0 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/qwen3/) and the [Qwen3-32B model page](https://whatcanirunai.vercel.app/models/qwen3-32b/).

## Other hardware for Qwen3-32B

* [Qwen3-32B on an RTX 3060 12GB](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/rtx-3060-12gb/)Won’t fit
* [Qwen3-32B on an RTX 3070 Ti](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/rtx-3070-ti/)Won’t fit
* [Qwen3-32B on an RTX 3080](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/rtx-3080-10gb/)Won’t fit
* [Qwen3-32B on an RTX 4060 Ti 16GB](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/rtx-4060-ti-16gb/)Won’t fit
* [Qwen3-32B on an RTX 4070 Ti Super](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/rtx-4070-ti-super/)Won’t fit
* [Qwen3-32B on an RTX 4090](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/rtx-4090/)Tight

## Other models on an RTX 3090

* [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
* [Gemma 4 31B on an RTX 3090](https://whatcanirunai.vercel.app/can-i-run/gemma-4-31b-it/rtx-3090/)Tight
* [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 Qwen3-32B on an RTX 3090?

Yes, but it is tight. Qwen3-32B at Q4\_K\_M needs about 23.8 GB, and the GeForce RTX 3090 has 24.0 GB usable.

How much VRAM does Qwen3-32B need?

About 23.8 GB at Q4\_K\_M, 41.0 GB at Q8\_0 and 76.4 GB at FP16, estimated for a \~4k context.

How fast is Qwen3-32B on an RTX 3090?

Our estimate is 28–40 tokens per second at Q4\_K\_M. It is not a measured benchmark.

What context length can I use?

At Q4\_K\_M, our KV estimate supports up to about 4k tokens before the cache pushes the total over usable memory.

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