# Can I run Qwen3-30B-A3B on a Mac mini 24GB?

> No. Qwen3-30B-A3B needs about 22.3 GB at Q4_K_M, and Mac mini 24GB has 18.0 GB usable. What to run instead.

Source: https://whatcanirunai.vercel.app/can-i-run/qwen3-30b-a3b/mac-mini-m6-24/

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**No.** Qwen3-30B-A3B at Q4\_K\_M needs about 22.3 GB, and the Mac mini M6 24GB has 18.0 GB usable at a \~4k context.

# Can I run Qwen3-30B-A3B on a Mac mini 24GB?

Qwen3-30B-A3B at Q4\_K\_M needs about 22.3 GB, and the Mac mini M6 24GB has 18.0 GB usable. That figure is 24 GB of unified memory on the spec sheet, of which 18.0 GB is usable for weights and cache, at 170 GB/s memory bandwidth. A Qwen3 Mixture-of-Experts with 30B total weights and about 3B active per token, so it needs 30B-class memory but can decode closer to a small dense model. These are estimates from [our formula](https://whatcanirunai.vercel.app/method/), not measured benchmarks.

Quant that fits

None of Q4, Q8 or FP16 needs 22.3 GB

Memory needed

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

Usable unified memory

18.0 GB of 24 GB unified memory

Est. tokens/sec

n/a does not fit

[Qwen3-30B-A3B](https://whatcanirunai.vercel.app/models/qwen3-30b-a3b/) · [Mac mini M6 24GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m6-24/) · [Everything this machine runs](https://whatcanirunai.vercel.app/what-can-i-run/mac-mini-m6-24/) · [Q4 vs Q8 vs FP16](https://whatcanirunai.vercel.app/quantization/)

## Memory at each quant

| Quant    | Need vs 18.0 GB | Verdict       |
| -------- | --------------- | ------------- |
| Q4\_K\_M | 22.3 GB         | Q4Won’t fit   |
| Q8\_0    | 38.3 GB         | Q8Won’t fit   |
| FP16     | 71.2 GB         | FP16Won’t fit |

## Estimated tokens per second

No speed estimate: Qwen3-30B-A3B does not fit in the 18.0 GB this machine can use, so we do not publish a tokens/sec figure for CPU offload.

## Context headroom

Even at a short \~4k context, the weights do not fit. Extending context would only increase the gap.

__Context headroom for Qwen3-30B-A3B on a Mac mini 24GB at Q4\_K\_M__
| Context | KV cache (FP16) | Total at Q4\_K\_M | Verdict   |
| ------- | --------------- | ----------------- | --------- |
| 4k      | 0.4 GB          | 22.3 GB           | Won’t fit |
| 8k      | 0.8 GB          | 22.7 GB           | Won’t fit |
| 16k     | 1.6 GB          | 23.5 GB           | Won’t fit |
| 32k     | 3.2 GB          | 25.1 GB           | Won’t fit |
| 64k     | 6.4 GB          | 28.3 GB           | Won’t fit |
| 128k    | 12.9 GB         | 34.8 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 [Mistral Small 24B](https://whatcanirunai.vercel.app/can-i-run/mistral-small-3.2-24b-instruct-2506/mac-mini-m6-24/) instead (17.7 GB at Q4). It is a different model, not a drop-in, but it does fit this machine.
* To hold Qwen3-30B-A3B at Q4, you need about 22.3 GB. The [GeForce RTX 3090](https://whatcanirunai.vercel.app/can-i-run/qwen3-30b-a3b/rtx-3090/) has 24.0 GB usable ($1499 list price).
* CPU offload can load the weights anyway, but it is slow. We do not publish an offload tokens/sec figure.
* See the [best GPU for this model family](https://whatcanirunai.vercel.app/best-gpu-for/qwen3/) and the [Qwen3-30B-A3B model page](https://whatcanirunai.vercel.app/models/qwen3-30b-a3b/).

## Other hardware for Qwen3-30B-A3B

* [Qwen3-30B-A3B on an RTX 3060 12GB](https://whatcanirunai.vercel.app/can-i-run/qwen3-30b-a3b/rtx-3060-12gb/)Won’t fit
* [Qwen3-30B-A3B on an RTX 3070 Ti](https://whatcanirunai.vercel.app/can-i-run/qwen3-30b-a3b/rtx-3070-ti/)Won’t fit
* [Qwen3-30B-A3B on an RTX 3080](https://whatcanirunai.vercel.app/can-i-run/qwen3-30b-a3b/rtx-3080-10gb/)Won’t fit
* [Qwen3-30B-A3B on an RTX 3090](https://whatcanirunai.vercel.app/can-i-run/qwen3-30b-a3b/rtx-3090/)Tight
* [Qwen3-30B-A3B on an RTX 4060 Ti 16GB](https://whatcanirunai.vercel.app/can-i-run/qwen3-30b-a3b/rtx-4060-ti-16gb/)Won’t fit
* [Qwen3-30B-A3B on an RTX 4070 Ti Super](https://whatcanirunai.vercel.app/can-i-run/qwen3-30b-a3b/rtx-4070-ti-super/)Won’t fit

## Other models on a Mac mini 24GB

* [Qwen3-32B on a Mac mini 24GB](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/mac-mini-m6-24/)Won’t fit
* [Qwen3.8-27B on a Mac mini 24GB](https://whatcanirunai.vercel.app/can-i-run/qwen3.8-27b/mac-mini-m6-24/)Won’t fit
* [Gemma 3 27B on a Mac mini 24GB](https://whatcanirunai.vercel.app/can-i-run/gemma-3-27b-it/mac-mini-m6-24/)Won’t fit
* [Gemma 4 31B on a Mac mini 24GB](https://whatcanirunai.vercel.app/can-i-run/gemma-4-31b-it/mac-mini-m6-24/)Won’t fit
* [gpt-oss-20b on a Mac mini 24GB](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-20b/mac-mini-m6-24/)Fits
* [Mistral Small 24B on a Mac mini 24GB](https://whatcanirunai.vercel.app/can-i-run/mistral-small-3.2-24b-instruct-2506/mac-mini-m6-24/)Tight

## Quick answers

Can I run Qwen3-30B-A3B on a Mac mini 24GB?

No. Qwen3-30B-A3B at Q4\_K\_M needs about 22.3 GB, and the Mac mini M6 24GB has 18.0 GB usable.

How much VRAM does Qwen3-30B-A3B need?

About 22.3 GB at Q4\_K\_M, 38.3 GB at Q8\_0 and 71.2 GB at FP16, estimated for a \~4k context.

How fast is Qwen3-30B-A3B on a Mac mini 24GB?

We do not estimate tokens/sec when the model does not fit. CPU offload is possible but slow.

What context length can I use?

The weights already do not fit, so longer context is not the limiting factor.

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