# Can I run Gemma 3 4B on a Mac mini 16GB? | What Can I Run AI

> Yes, but it is tight. Gemma 3 4B at FP16 needs about 10.9 GB; Mac mini 16GB has 12.0 GB usable. Estimated tokens/sec and context headroom.

Source: https://whatcanirunai.vercel.app/can-i-run/gemma-3-4b-it/mac-mini-m6-16/

[Home](https://whatcanirunai.vercel.app/) / [Can I run it?](https://whatcanirunai.vercel.app/can-i-run/) / [Gemma 3 4B](https://whatcanirunai.vercel.app/models/gemma-3-4b-it/) / Gemma 3 4B on a Mac mini 16GB

**Yes, but it is tight.** Gemma 3 4B at FP16 needs about 10.9 GB, and the Mac mini M6 16GB has 12.0 GB usable at a \~4k context.

# Can I run Gemma 3 4B on a Mac mini 16GB?

Gemma 3 4B at FP16 needs about 10.9 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. Google’s small Gemma 3 instruct model, a good low-VRAM option with a different flavor from Llama 3.2 3B. These are estimates from [our formula](https://whatcanirunai.vercel.app/method/), not measured benchmarks.

Quant that fits

FP16 Fits, short context only

Memory needed

10.9 GB at FP16, \~4k context

Usable unified memory

12.0 GB of 16 GB unified memory

Est. tokens/sec

8–14 estimate

[gemma-3-4b-it](https://whatcanirunai.vercel.app/models/gemma-3-4b-it/) · [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 | 4.0 GB          | Q4Fits    |
| Q8\_0    | 6.3 GB          | Q8Fits    |
| FP16     | 10.9 GB         | FP16Tight |

## Estimated tokens per second

Estimate: **8–14 tokens/sec** at FP16\. This is a bandwidth-derived range, not a bench. Efficiency is a 45–80% unified-memory band from [How we calculate](https://whatcanirunai.vercel.app/method/).

## Context headroom

We do not have a published KV config for this model, so the memory need is the \~4k-context estimate only. Use the [VRAM calculator](https://whatcanirunai.vercel.app/vram-calculator/) to explore longer chats.

## What to do instead

* Run [Llama 3.2 3B](https://whatcanirunai.vercel.app/can-i-run/llama-3.2-3b-instruct/mac-mini-m6-16/) instead (3.2 GB at Q4). It is a different model, not a drop-in, but it does fit this machine.
* Keep context short, close other GPU work, and avoid a higher quant. The [VRAM calculator](https://whatcanirunai.vercel.app/vram-calculator/) shows how the KV cache grows.
* See the [best GPU for this model family](https://whatcanirunai.vercel.app/best-gpu-for/gemma-3/) and the [gemma-3-4b-it model page](https://whatcanirunai.vercel.app/models/gemma-3-4b-it/).

## Other hardware for Gemma 3 4B

* [Gemma 3 4B on an RTX 3060 12GB](https://whatcanirunai.vercel.app/can-i-run/gemma-3-4b-it/rtx-3060-12gb/)Tight
* [Gemma 3 4B on an RTX 3070 Ti](https://whatcanirunai.vercel.app/can-i-run/gemma-3-4b-it/rtx-3070-ti/)Fits
* [Gemma 3 4B on an RTX 4060 Ti 16GB](https://whatcanirunai.vercel.app/can-i-run/gemma-3-4b-it/rtx-4060-ti-16gb/)Fits
* [Gemma 3 4B on an RTX 4090](https://whatcanirunai.vercel.app/can-i-run/gemma-3-4b-it/rtx-4090/)Fits
* [Gemma 3 4B on an Arc B580](https://whatcanirunai.vercel.app/can-i-run/gemma-3-4b-it/arc-b580/)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
* [gpt-oss-20b on a Mac mini 16GB](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-20b/mac-mini-m6-16/)Won’t fit

## Quick answers

Can I run Gemma 3 4B on a Mac mini 16GB?

Yes, but it is tight. Gemma 3 4B at FP16 needs about 10.9 GB, and the Mac mini M6 16GB has 12.0 GB usable.

How much VRAM does Gemma 3 4B need?

About 4.0 GB at Q4\_K\_M, 6.3 GB at Q8\_0 and 10.9 GB at FP16, estimated for a \~4k context.

How fast is Gemma 3 4B on a Mac mini 16GB?

Our estimate is 8–14 tokens per second at FP16\. It is not a measured benchmark.

---
Agent guide: https://whatcanirunai.vercel.app/llms.txt · JSON API: https://whatcanirunai.vercel.app/developers/