# Can I run Qwen3-235B on a Mac Studio 512GB?

> Yes. Qwen3-235B at Q8_0 needs about 288.3 GB; Mac Studio 512GB has 384.0 GB usable. Estimated tokens/sec and context headroom.

Source: https://whatcanirunai.vercel.app/can-i-run/qwen3-235b-a22b/mac-studio-m5-ultra-512/

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**Yes.** Qwen3-235B at Q8\_0 needs about 288.3 GB, and the Mac Studio M5 Ultra 512GB has 384.0 GB usable at a \~4k context.

# Can I run Qwen3-235B on a Mac Studio 512GB?

Qwen3-235B at Q8\_0 needs about 288.3 GB, and the Mac Studio M5 Ultra 512GB has 384.0 GB usable. That figure is 512 GB of unified memory on the spec sheet, of which 384.0 GB is usable for weights and cache, at 1200 GB/s memory bandwidth. Qwen3’s large Mixture-of-Experts (235B total, about 22B active). It needs a high-memory unified machine at Q4, not a single consumer GPU. These are estimates from [our formula](https://whatcanirunai.vercel.app/method/), not measured benchmarks.

Quant that fits

Q8\_0 Fits with headroom

Memory needed

288.3 GB at Q8\_0, \~4k context

Usable unified memory

384.0 GB of 512 GB unified memory

Est. tokens/sec

up to 41 estimate

[Qwen3-235B-A22B](https://whatcanirunai.vercel.app/models/qwen3-235b-a22b/) · [Mac Studio M5 Ultra 512GB](https://whatcanirunai.vercel.app/hardware/mac-studio-m5-ultra-512/) · [Everything this machine runs](https://whatcanirunai.vercel.app/what-can-i-run/mac-studio-m5-ultra-512/) · [Q4 vs Q8 vs FP16](https://whatcanirunai.vercel.app/quantization/)

## Memory at each quant

| Quant    | Need vs 384.0 GB | Verdict       |
| -------- | ---------------- | ------------- |
| Q4\_K\_M | 164.9 GB         | Q4Fits        |
| Q8\_0    | 288.3 GB         | Q8Fits        |
| FP16     | 541.7 GB         | FP16Won’t fit |

## Estimated tokens per second

Estimate: **up to 41 tokens/sec** at Q8\_0 (upper bound: this is a Mixture-of-Experts, and only the active weights are counted). 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 Q8\_0, this machine has headroom out to about **128k tokens** under our KV estimate. Longer chats need more memory for the cache. Figures assume an FP16 KV cache.

__Context headroom for Qwen3-235B on a Mac Studio 512GB at Q8\_0__
| Context | KV cache (FP16) | Total at Q8\_0 | Verdict |
| ------- | --------------- | -------------- | ------- |
| 4k      | 0.8 GB          | 288.3 GB       | Fits    |
| 8k      | 1.6 GB          | 289.0 GB       | Fits    |
| 16k     | 3.2 GB          | 290.6 GB       | Fits    |
| 32k     | 6.3 GB          | 293.8 GB       | Fits    |
| 64k     | 12.6 GB         | 300.1 GB       | Fits    |
| 128k    | 25.2 GB         | 312.7 GB       | Fits    |

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/qwen3/) and the [Qwen3-235B-A22B model page](https://whatcanirunai.vercel.app/models/qwen3-235b-a22b/).

## Other hardware for Qwen3-235B

* [Qwen3-235B on a Mac Studio 256GB](https://whatcanirunai.vercel.app/can-i-run/qwen3-235b-a22b/mac-studio-m5-ultra-256/)Fits
* [Qwen3-235B on dual RTX 3090s](https://whatcanirunai.vercel.app/can-i-run/qwen3-235b-a22b/dual-rtx-3090/)Won’t fit
* [Qwen3-235B on an RTX 5090](https://whatcanirunai.vercel.app/can-i-run/qwen3-235b-a22b/rtx-5090/)Won’t fit
* [Qwen3-235B on an RTX 4090](https://whatcanirunai.vercel.app/can-i-run/qwen3-235b-a22b/rtx-4090/)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

## Other models on a Mac Studio 512GB

* [Llama 3.3 70B on a Mac Studio 512GB](https://whatcanirunai.vercel.app/can-i-run/llama-3.3-70b-instruct/mac-studio-m5-ultra-512/)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
* [DeepSeek-R1 on a Mac Studio 512GB](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1/mac-studio-m5-ultra-512/)Won’t fit
* [DeepSeek-V3.2 on a Mac Studio 512GB](https://whatcanirunai.vercel.app/can-i-run/deepseek-v3.2/mac-studio-m5-ultra-512/)Won’t fit

## Quick answers

Can I run Qwen3-235B on a Mac Studio 512GB?

Yes. Qwen3-235B at Q8\_0 needs about 288.3 GB, and the Mac Studio M5 Ultra 512GB has 384.0 GB usable.

How much VRAM does Qwen3-235B need?

About 164.9 GB at Q4\_K\_M, 288.3 GB at Q8\_0 and 541.7 GB at FP16, estimated for a \~4k context.

How fast is Qwen3-235B on a Mac Studio 512GB?

Our estimate is up to 41 tokens per second at Q8\_0, as an upper bound for this MoE. It is not a measured benchmark.

What context length can I use?

At Q8\_0, our KV estimate supports up to about 128k 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/