# Can I run gpt-oss-120b on a Mac mini 64GB? | What Can I Run?

> No. gpt-oss-120b needs about 82.5 GB at Q4_K_M, and Mac mini 64GB has 48.0 GB usable. What to run instead.

Source: https://whatcanirunai.vercel.app/can-i-run/gpt-oss-120b/mac-mini-m5-pro-64/

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**No.** gpt-oss-120b at Q4\_K\_M needs about 82.5 GB, and the Mac mini M5 Pro 64GB has 48.0 GB usable at a \~4k context.

# Can I run gpt-oss-120b on a Mac mini 64GB?

gpt-oss-120b at Q4\_K\_M needs about 82.5 GB, and the Mac mini M5 Pro 64GB has 48.0 GB usable. That figure is 64 GB of unified memory on the spec sheet, of which 48.0 GB is usable for weights and cache, at 307 GB/s memory bandwidth. OpenAI’s open 120B-class gpt-oss model. It needs large unified memory at Q4, with a much smaller active set per token. These are estimates from [our formula](https://whatcanirunai.vercel.app/method/), not measured benchmarks.

Quant that fits

None of Q4, Q8 or FP16 needs 82.5 GB

Memory needed

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

Usable unified memory

48.0 GB of 64 GB unified memory

Est. tokens/sec

n/a does not fit

[gpt-oss-120b](https://whatcanirunai.vercel.app/models/gpt-oss-120b/) · [Mac mini M5 Pro 64GB](https://whatcanirunai.vercel.app/hardware/mac-mini-m5-pro-64/) · [Everything this machine runs](https://whatcanirunai.vercel.app/what-can-i-run/mac-mini-m5-pro-64/) · [Q4 vs Q8 vs FP16](https://whatcanirunai.vercel.app/quantization/)

## Memory at each quant

| Quant    | Need vs 48.0 GB | Verdict       |
| -------- | --------------- | ------------- |
| Q4\_K\_M | 82.5 GB         | Q4Won’t fit   |
| Q8\_0    | 143.8 GB        | Q8Won’t fit   |
| FP16     | 269.7 GB        | FP16Won’t fit |

## Estimated tokens per second

No speed estimate: gpt-oss-120b does not fit in the 48.0 GB this machine can use, so we do not publish a tokens/sec figure for CPU offload.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-120b on a Mac mini 64GB at Q4\_K\_M__
| Context | KV cache (FP16) | Total at Q4\_K\_M | Verdict   |
| ------- | --------------- | ----------------- | --------- |
| 4k      | 0.2 GB          | 82.5 GB           | Won’t fit |
| 8k      | 0.3 GB          | 82.6 GB           | Won’t fit |
| 16k     | 0.6 GB          | 82.9 GB           | Won’t fit |
| 32k     | 1.2 GB          | 83.5 GB           | Won’t fit |
| 64k     | 2.4 GB          | 84.7 GB           | Won’t fit |
| 128k    | 4.8 GB          | 87.1 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 [R1 Distill 32B](https://whatcanirunai.vercel.app/models/deepseek-r1-distill-qwen-32b/) instead (23.8 GB at Q4). It is a different model, not a drop-in, but it does fit this machine.
* To hold gpt-oss-120b at Q4, you need about 82.5 GB. The [MacBook Pro M5 Max 128GB](https://whatcanirunai.vercel.app/hardware/macbook-pro-m5-max-128/) has 96.0 GB usable ($6699 Apple list price, 14-inch M5 Max 18C CPU/40C GPU, 128GB/2TB).
* 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-120b model page](https://whatcanirunai.vercel.app/models/gpt-oss-120b/).

## Other hardware for gpt-oss-120b

* [gpt-oss-120b on an RTX 4090](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-120b/rtx-4090/)Won’t fit
* [gpt-oss-120b on an RTX 5090](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-120b/rtx-5090/)Won’t fit
* [gpt-oss-120b on an RTX 3090](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-120b/rtx-3090/)Won’t fit
* [gpt-oss-120b on dual RTX 3090s](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-120b/dual-rtx-3090/)Won’t fit
* [gpt-oss-120b on a Mac Studio 256GB](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-120b/mac-studio-m5-ultra-256/)Fits
* [gpt-oss-120b on a Mac Studio 512GB](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-120b/mac-studio-m5-ultra-512/)Fits

## Other models on a Mac mini 64GB

* [Llama 3.3 70B on a Mac mini 64GB](https://whatcanirunai.vercel.app/can-i-run/llama-3.3-70b-instruct/mac-mini-m5-pro-64/)Won’t fit
* [R1 Distill 70B on a Mac mini 64GB](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1-distill-llama-70b/mac-mini-m5-pro-64/)Won’t fit
* [Qwen3-235B on a Mac mini 64GB](https://whatcanirunai.vercel.app/can-i-run/qwen3-235b-a22b/mac-mini-m5-pro-64/)Won’t fit
* [Mixtral 8x22B on a Mac mini 64GB](https://whatcanirunai.vercel.app/can-i-run/mixtral-8x22b-instruct-v0.1/mac-mini-m5-pro-64/)Won’t fit
* [GLM-4.5-Air on a Mac mini 64GB](https://whatcanirunai.vercel.app/can-i-run/glm-4.5-air/mac-mini-m5-pro-64/)Won’t fit

## Quick answers

Can I run gpt-oss-120b on a Mac mini 64GB?

No. gpt-oss-120b at Q4\_K\_M needs about 82.5 GB, and the Mac mini M5 Pro 64GB has 48.0 GB usable.

How much VRAM does gpt-oss-120b need?

About 82.5 GB at Q4\_K\_M, 143.8 GB at Q8\_0 and 269.7 GB at FP16, estimated for a \~4k context.

How fast is gpt-oss-120b on a Mac mini 64GB?

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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