# Can I run gpt-oss-20b on a Mac mini 16GB? | What Can I Run?

> No. gpt-oss-20b needs about 15.6 GB at Q4_K_M, and Mac mini 16GB has 12.0 GB usable. What to run instead.

Source: https://whatcanirunai.vercel.app/can-i-run/gpt-oss-20b/mac-mini-m6-16/

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**No.** gpt-oss-20b at Q4\_K\_M needs about 15.6 GB, and the Mac mini M6 16GB has 12.0 GB usable at a \~4k context.

# Can I run gpt-oss-20b on a Mac mini 16GB?

gpt-oss-20b at Q4\_K\_M needs about 15.6 GB, and the Mac mini M6 16GB has 12.0 GB usable. That figure is 16 GB of unified memory on the spec sheet, of which 12.0 GB is usable for weights and cache, at 153 GB/s memory bandwidth. OpenAI’s open 20B-class gpt-oss model. Only a few billion parameters are active per token, so decode can be quicker than a dense 20B. These are estimates from [our formula](https://whatcanirunai.vercel.app/method/), not measured benchmarks.

Quant that fits

None of Q4, Q8 or FP16 needs 15.6 GB

Memory needed

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

Usable unified memory

12.0 GB of 16 GB unified memory

Est. tokens/sec

n/a does not fit

[gpt-oss-20b](https://whatcanirunai.vercel.app/models/gpt-oss-20b/) · [Mac mini M6 16GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m6-16/) · [Everything this machine runs](https://whatcanirunai.vercel.app/what-can-i-run/mac-mini-m6-16/) · [Q4 vs Q8 vs FP16](https://whatcanirunai.vercel.app/quantization/)

## Memory at each quant

| Quant    | Need vs 12.0 GB | Verdict       |
| -------- | --------------- | ------------- |
| Q4\_K\_M | 15.6 GB         | Q4Won’t fit   |
| Q8\_0    | 26.6 GB         | Q8Won’t fit   |
| FP16     | 49.1 GB         | FP16Won’t fit |

## Estimated tokens per second

No speed estimate: gpt-oss-20b does not fit in the 12.0 GB this machine can use, so we do not publish a tokens/sec figure for CPU offload.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 gpt-oss-20b on a Mac mini 16GB at Q4\_K\_M__
| Context | KV cache (FP16) | Total at Q4\_K\_M | Verdict   |
| ------- | --------------- | ----------------- | --------- |
| 4k      | 0.1 GB          | 15.6 GB           | Won’t fit |
| 8k      | 0.2 GB          | 15.7 GB           | Won’t fit |
| 16k     | 0.4 GB          | 15.9 GB           | Won’t fit |
| 32k     | 0.8 GB          | 16.3 GB           | Won’t fit |
| 64k     | 1.6 GB          | 17.1 GB           | Won’t fit |
| 128k    | 3.2 GB          | 18.7 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 [R1 Distill 14B](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1-distill-qwen-14b/mac-mini-m6-16/) instead (11.3 GB at Q4). It is a different model, not a drop-in, but it does fit this machine.
* To hold gpt-oss-20b at Q4, you need about 15.6 GB. The [GeForce RTX 4060 Ti 16GB](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-20b/rtx-4060-ti-16gb/) has 16.0 GB usable ($499 list price).
* Our estimate is only 3.6 GB over this machine’s usable memory, so a tighter quant or a shorter context may work in practice. We still mark it as a no under [the published formula](https://whatcanirunai.vercel.app/method/).
* 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/gpt-oss/) and the [gpt-oss-20b model page](https://whatcanirunai.vercel.app/models/gpt-oss-20b/).

## Other hardware for gpt-oss-20b

* [gpt-oss-20b on an RTX 3060 12GB](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-20b/rtx-3060-12gb/)Won’t fit
* [gpt-oss-20b on an RTX 3070 Ti](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-20b/rtx-3070-ti/)Won’t fit
* [gpt-oss-20b on an RTX 3080](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-20b/rtx-3080-10gb/)Won’t fit
* [gpt-oss-20b on an RTX 3090](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-20b/rtx-3090/)Fits
* [gpt-oss-20b on an RTX 4060 Ti 16GB](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-20b/rtx-4060-ti-16gb/)Tight
* [gpt-oss-20b on an RTX 4070 Ti Super](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-20b/rtx-4070-ti-super/)Tight

## Other models on a Mac mini 16GB

* [Qwen3-32B on a Mac mini 16GB](https://whatcanirunai.vercel.app/can-i-run/qwen3-32b/mac-mini-m6-16/)Won’t fit
* [Qwen3-30B-A3B on a Mac mini 16GB](https://whatcanirunai.vercel.app/can-i-run/qwen3-30b-a3b/mac-mini-m6-16/)Won’t fit
* [Qwen3.8-27B on a Mac mini 16GB](https://whatcanirunai.vercel.app/can-i-run/qwen3.8-27b/mac-mini-m6-16/)Won’t fit
* [Gemma 3 27B on a Mac mini 16GB](https://whatcanirunai.vercel.app/can-i-run/gemma-3-27b-it/mac-mini-m6-16/)Won’t fit
* [Gemma 4 31B on a Mac mini 16GB](https://whatcanirunai.vercel.app/can-i-run/gemma-4-31b-it/mac-mini-m6-16/)Won’t fit
* [Mistral Small 24B on a Mac mini 16GB](https://whatcanirunai.vercel.app/can-i-run/mistral-small-3.2-24b-instruct-2506/mac-mini-m6-16/)Won’t fit

## Quick answers

Can I run gpt-oss-20b on a Mac mini 16GB?

No. gpt-oss-20b at Q4\_K\_M needs about 15.6 GB, and the Mac mini M6 16GB has 12.0 GB usable.

How much VRAM does gpt-oss-20b need?

About 15.6 GB at Q4\_K\_M, 26.6 GB at Q8\_0 and 49.1 GB at FP16, estimated for a \~4k context.

How fast is gpt-oss-20b on a Mac mini 16GB?

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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