# Can I run Llama 3.3 70B on a Mac Studio 256GB?

> Yes. Llama 3.3 70B at FP16 needs about 163.3 GB; Mac Studio 256GB has 192.0 GB usable. Estimated tokens/sec and context headroom.

Source: https://whatcanirunai.vercel.app/can-i-run/llama-3.3-70b-instruct/mac-studio-m5-ultra-256/

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**Yes.** Llama 3.3 70B at FP16 needs about 163.3 GB, and the Mac Studio M5 Ultra 256GB has 192.0 GB usable at a \~4k context.

# Can I run Llama 3.3 70B on a Mac Studio 256GB?

Llama 3.3 70B at FP16 needs about 163.3 GB, and the Mac Studio M5 Ultra 256GB has 192.0 GB usable. That figure is 256 GB of unified memory on the spec sheet, of which 192.0 GB is usable for weights and cache, at 1200 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

FP16 Fits with headroom

Memory needed

163.3 GB at FP16, \~4k context

Usable unified memory

192.0 GB of 256 GB unified memory

Est. tokens/sec

4–7 estimate

[Llama-3.3-70B-Instruct](https://whatcanirunai.vercel.app/models/llama-3.3-70b-instruct/) · [Mac Studio M5 Ultra 256GB](https://whatcanirunai.vercel.app/hardware/mac-studio-m5-ultra-256/) · [Everything this machine runs](https://whatcanirunai.vercel.app/what-can-i-run/mac-studio-m5-ultra-256/) · [Q4 vs Q8 vs FP16](https://whatcanirunai.vercel.app/quantization/)

## Memory at each quant

| Quant    | Need vs 192.0 GB | Verdict  |
| -------- | ---------------- | -------- |
| Q4\_K\_M | 50.2 GB          | Q4Fits   |
| Q8\_0    | 87.2 GB          | Q8Fits   |
| FP16     | 163.3 GB         | FP16Fits |

## Estimated tokens per second

Estimate: **4–7 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

At FP16, this machine has headroom out to about **64k tokens** under our KV estimate. Longer chats need more memory for the cache. Figures assume an FP16 KV cache.

__Context headroom for Llama 3.3 70B on a Mac Studio 256GB at FP16__
| Context | KV cache (FP16) | Total at FP16 | Verdict   |
| ------- | --------------- | ------------- | --------- |
| 4k      | 1.3 GB          | 163.3 GB      | Fits      |
| 8k      | 2.7 GB          | 164.6 GB      | Fits      |
| 16k     | 5.4 GB          | 167.3 GB      | Fits      |
| 32k     | 10.7 GB         | 172.7 GB      | Fits      |
| 64k     | 21.5 GB         | 183.4 GB      | Tight     |
| 128k    | 42.9 GB         | 204.9 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

* 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 Studio 256GB

* [R1 Distill 70B on a Mac Studio 256GB](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1-distill-llama-70b/mac-studio-m5-ultra-256/)Fits
* [gpt-oss-120b on a Mac Studio 256GB](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-120b/mac-studio-m5-ultra-256/)Fits
* [Qwen3-235B on a Mac Studio 256GB](https://whatcanirunai.vercel.app/can-i-run/qwen3-235b-a22b/mac-studio-m5-ultra-256/)Fits
* [DeepSeek-R1 on a Mac Studio 256GB](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1/mac-studio-m5-ultra-256/)Won’t fit
* [DeepSeek-V3.2 on a Mac Studio 256GB](https://whatcanirunai.vercel.app/can-i-run/deepseek-v3.2/mac-studio-m5-ultra-256/)Won’t fit
* [Mixtral 8x22B on a Mac Studio 256GB](https://whatcanirunai.vercel.app/can-i-run/mixtral-8x22b-instruct-v0.1/mac-studio-m5-ultra-256/)Tight

## Quick answers

Can I run Llama 3.3 70B on a Mac Studio 256GB?

Yes. Llama 3.3 70B at FP16 needs about 163.3 GB, and the Mac Studio M5 Ultra 256GB has 192.0 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 Studio 256GB?

Our estimate is 4–7 tokens per second at FP16\. It is not a measured benchmark.

What context length can I use?

At FP16, our KV estimate supports up to about 64k tokens before the cache pushes the total over usable memory.

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