# Can I run R1 Distill 32B on a Mac mini 24GB?

> No. R1 Distill 32B needs about 23.8 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/deepseek-r1-distill-qwen-32b/mac-mini-m6-24/

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**No.** R1 Distill 32B at Q4\_K\_M needs about 23.8 GB, and the Mac mini M6 24GB has 18.0 GB usable at a \~4k context.

# Can I run R1 Distill 32B on a Mac mini 24GB?

R1 Distill 32B at Q4\_K\_M needs about 23.8 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 32B DeepSeek-R1 distill on Qwen. Memory need matches Qwen3-32B; the weights are distilled from DeepSeek-R1\. These are estimates from [our formula](https://whatcanirunai.vercel.app/method/), not measured benchmarks.

Quant that fits

None of Q4, Q8 or FP16 needs 23.8 GB

Memory needed

23.8 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

[DeepSeek-R1-Distill-Qwen-32B](https://whatcanirunai.vercel.app/models/deepseek-r1-distill-qwen-32b/) · [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 | 23.8 GB         | Q4Won’t fit   |
| Q8\_0    | 41.0 GB         | Q8Won’t fit   |
| FP16     | 76.4 GB         | FP16Won’t fit |

## Estimated tokens per second

No speed estimate: R1 Distill 32B does not fit in the 18.0 GB this machine can use, so we do not publish a tokens/sec figure for CPU offload.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 R1 Distill 32B on a Mac mini 24GB at Q4\_K\_M__
| Context | KV cache (FP16) | Total at Q4\_K\_M | Verdict   |
| ------- | --------------- | ----------------- | --------- |
| 4k      | 1.1 GB          | 23.8 GB           | Won’t fit |
| 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 [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 R1 Distill 32B at Q4, you need about 23.8 GB. The [GeForce RTX 3090](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1-distill-qwen-32b/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/deepseek-r1/) and the [DeepSeek-R1-Distill-Qwen-32B model page](https://whatcanirunai.vercel.app/models/deepseek-r1-distill-qwen-32b/).

## Other hardware for R1 Distill 32B

* [R1 Distill 32B on an RTX 4090](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1-distill-qwen-32b/rtx-4090/)Tight
* [R1 Distill 32B on an RTX 3090](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1-distill-qwen-32b/rtx-3090/)Tight
* [R1 Distill 32B on an RTX 5080](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1-distill-qwen-32b/rtx-5080/)Won’t fit
* [R1 Distill 32B on an RTX 5090](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1-distill-qwen-32b/rtx-5090/)Fits
* [R1 Distill 32B on an RX 7900 XTX](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1-distill-qwen-32b/rx-7900-xtx/)Tight

## 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
* [Qwen3-30B-A3B on a Mac mini 24GB](https://whatcanirunai.vercel.app/can-i-run/qwen3-30b-a3b/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

## Quick answers

Can I run R1 Distill 32B on a Mac mini 24GB?

No. R1 Distill 32B at Q4\_K\_M needs about 23.8 GB, and the Mac mini M6 24GB has 18.0 GB usable.

How much VRAM does R1 Distill 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 R1 Distill 32B 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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