# Can I run Llama 3.3 70B on a Mac mini 64GB?

> No. Llama 3.3 70B needs about 50.2 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/llama-3.3-70b-instruct/mac-mini-m5-pro-64/

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**No.** Llama 3.3 70B at Q4\_K\_M needs about 50.2 GB, and the Mac mini M5 Pro 64GB has 48.0 GB usable at a \~4k context.

# Can I run Llama 3.3 70B on a Mac mini 64GB?

Llama 3.3 70B at Q4\_K\_M needs about 50.2 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. Meta’s flagship 70B instruct model, the usual target for a high-end local chat box. These are estimates from [our formula](https://whatcanirunai.vercel.app/method/), not measured benchmarks.

Quant that fits

None of Q4, Q8 or FP16 needs 50.2 GB

Memory needed

50.2 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

[Llama-3.3-70B-Instruct](https://whatcanirunai.vercel.app/models/llama-3.3-70b-instruct/) · [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 | 50.2 GB         | Q4Won’t fit   |
| Q8\_0    | 87.2 GB         | Q8Won’t fit   |
| FP16     | 163.3 GB        | FP16Won’t fit |

## Estimated tokens per second

No speed estimate: Llama 3.3 70B does not fit in the 48.0 GB this machine can use, so we do not publish a tokens/sec figure for CPU offload.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 Llama 3.3 70B on a Mac mini 64GB at Q4\_K\_M__
| Context | KV cache (FP16) | Total at Q4\_K\_M | Verdict   |
| ------- | --------------- | ----------------- | --------- |
| 4k      | 1.3 GB          | 50.2 GB           | Won’t fit |
| 8k      | 2.7 GB          | 51.5 GB           | Won’t fit |
| 16k     | 5.4 GB          | 54.2 GB           | Won’t fit |
| 32k     | 10.7 GB         | 59.6 GB           | Won’t fit |
| 64k     | 21.5 GB         | 70.3 GB           | Won’t fit |
| 128k    | 42.9 GB         | 91.8 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 [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 Llama 3.3 70B at Q4, you need about 50.2 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).
* Our estimate is only 2.2 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/llama/) and the [Llama-3.3-70B-Instruct model page](https://whatcanirunai.vercel.app/models/llama-3.3-70b-instruct/).

## Other hardware for Llama 3.3 70B

* [Llama 3.3 70B on an RTX 3060 12GB](https://whatcanirunai.vercel.app/can-i-run/llama-3.3-70b-instruct/rtx-3060-12gb/)Won’t fit
* [Llama 3.3 70B on an RTX 3070 Ti](https://whatcanirunai.vercel.app/can-i-run/llama-3.3-70b-instruct/rtx-3070-ti/)Won’t fit
* [Llama 3.3 70B on an RTX 3080](https://whatcanirunai.vercel.app/can-i-run/llama-3.3-70b-instruct/rtx-3080-10gb/)Won’t fit
* [Llama 3.3 70B on an RTX 3090](https://whatcanirunai.vercel.app/can-i-run/llama-3.3-70b-instruct/rtx-3090/)Won’t fit
* [Llama 3.3 70B on an RTX 4060 Ti 16GB](https://whatcanirunai.vercel.app/can-i-run/llama-3.3-70b-instruct/rtx-4060-ti-16gb/)Won’t fit
* [Llama 3.3 70B on an RTX 4070 Ti Super](https://whatcanirunai.vercel.app/can-i-run/llama-3.3-70b-instruct/rtx-4070-ti-super/)Won’t fit

## Other models on a Mac mini 64GB

* [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
* [gpt-oss-120b on a Mac mini 64GB](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-120b/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 Llama 3.3 70B on a Mac mini 64GB?

No. Llama 3.3 70B at Q4\_K\_M needs about 50.2 GB, and the Mac mini M5 Pro 64GB has 48.0 GB usable.

How much VRAM does Llama 3.3 70B need?

About 50.2 GB at Q4\_K\_M, 87.2 GB at Q8\_0 and 163.3 GB at FP16, estimated for a \~4k context.

How fast is Llama 3.3 70B 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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Agent guide: https://whatcanirunai.vercel.app/llms.txt · JSON API: https://whatcanirunai.vercel.app/developers/