# Can I run GLM-4.5-Air on a Mac Studio 256GB?

> Yes. GLM-4.5-Air at Q8_0 needs about 136.0 GB; Mac Studio 256GB has 192.0 GB usable. Estimated tokens/sec and context headroom.

Source: https://whatcanirunai.vercel.app/can-i-run/glm-4.5-air/mac-studio-m5-ultra-256/

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**Yes.** GLM-4.5-Air at Q8\_0 needs about 136.0 GB, and the Mac Studio M5 Ultra 256GB has 192.0 GB usable at a \~4k context.

# Can I run GLM-4.5-Air on a Mac Studio 256GB?

GLM-4.5-Air at Q8\_0 needs about 136.0 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. Zhipu’s GLM-4.5-Air, a large model in the roughly 80 GB Q4 class — above a 24 GB card, below full DeepSeek-R1\. These are estimates from [our formula](https://whatcanirunai.vercel.app/method/), not measured benchmarks.

Quant that fits

Q8\_0 Fits with headroom

Memory needed

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

Usable unified memory

192.0 GB of 256 GB unified memory

Est. tokens/sec

up to 75 estimate

[GLM-4.5-Air](https://whatcanirunai.vercel.app/models/glm-4.5-air/) · [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 | 78.0 GB          | Q4Fits        |
| Q8\_0    | 136.0 GB         | Q8Fits        |
| FP16     | 255.1 GB         | FP16Won’t fit |

## Estimated tokens per second

Estimate: **up to 75 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 GLM-4.5-Air on a Mac Studio 256GB at Q8\_0__
| Context | KV cache (FP16) | Total at Q8\_0 | Verdict |
| ------- | --------------- | -------------- | ------- |
| 4k      | 0.8 GB          | 136.0 GB       | Fits    |
| 8k      | 1.5 GB          | 136.8 GB       | Fits    |
| 16k     | 3.1 GB          | 138.3 GB       | Fits    |
| 32k     | 6.2 GB          | 141.4 GB       | Fits    |
| 64k     | 12.3 GB         | 147.6 GB       | Fits    |
| 128k    | 24.7 GB         | 159.9 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

* Full hardware list: [GLM-4.5-Air](https://whatcanirunai.vercel.app/models/glm-4.5-air/). Specs for this machine: [Mac Studio M5 Ultra 256GB](https://whatcanirunai.vercel.app/hardware/mac-studio-m5-ultra-256/).

## Other hardware for GLM-4.5-Air

* [GLM-4.5-Air on an RTX 4090](https://whatcanirunai.vercel.app/can-i-run/glm-4.5-air/rtx-4090/)Won’t fit
* [GLM-4.5-Air on an RTX 5090](https://whatcanirunai.vercel.app/can-i-run/glm-4.5-air/rtx-5090/)Won’t fit
* [GLM-4.5-Air on dual RTX 3090s](https://whatcanirunai.vercel.app/can-i-run/glm-4.5-air/dual-rtx-3090/)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
* [GLM-4.5-Air on an RTX 3090](https://whatcanirunai.vercel.app/can-i-run/glm-4.5-air/rtx-3090/)Won’t fit

## Other models on a Mac Studio 256GB

* [Llama 3.3 70B on a Mac Studio 256GB](https://whatcanirunai.vercel.app/can-i-run/llama-3.3-70b-instruct/mac-studio-m5-ultra-256/)Fits
* [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

## Quick answers

Can I run GLM-4.5-Air on a Mac Studio 256GB?

Yes. GLM-4.5-Air at Q8\_0 needs about 136.0 GB, and the Mac Studio M5 Ultra 256GB has 192.0 GB usable.

How much VRAM does GLM-4.5-Air need?

About 78.0 GB at Q4\_K\_M, 136.0 GB at Q8\_0 and 255.1 GB at FP16, estimated for a \~4k context.

How fast is GLM-4.5-Air on a Mac Studio 256GB?

Our estimate is up to 75 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/