# Can I run GLM-4.5-Air on dual RTX 3090s? | What Can I Run AI

> No. GLM-4.5-Air needs about 78.0 GB at Q4_K_M, and dual RTX 3090s has 48.0 GB usable. What to run instead.

Source: https://whatcanirunai.vercel.app/can-i-run/glm-4.5-air/dual-rtx-3090/

[Home](https://whatcanirunai.vercel.app/) / [Can I run it?](https://whatcanirunai.vercel.app/can-i-run/) / [GLM-4.5-Air](https://whatcanirunai.vercel.app/models/glm-4.5-air/) / GLM-4.5-Air on dual RTX 3090s

**No.** GLM-4.5-Air at Q4\_K\_M needs about 78.0 GB, and dual RTX 3090s have 48.0 GB usable at a \~4k context.

# Can I run GLM-4.5-Air on dual RTX 3090s?

GLM-4.5-Air at Q4\_K\_M needs about 78.0 GB, and dual RTX 3090s have 48.0 GB usable. Two cards add memory, not decode speed; the speed estimate uses one RTX 3090’s 936 GB/s. 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

None of Q4, Q8 or FP16 needs 78.0 GB

Memory needed

78.0 GB at Q4\_K\_M, \~4k context

Usable VRAM

48.0 GB of 48 GB VRAM

Est. tokens/sec

n/a does not fit

[GLM-4.5-Air](https://whatcanirunai.vercel.app/models/glm-4.5-air/) · [GeForce RTX 3090](https://whatcanirunai.vercel.app/hardware/rtx-3090/) · [Multi-GPU](https://whatcanirunai.vercel.app/multi-gpu-for-local-llm/) · [Dual 3090 build](https://whatcanirunai.vercel.app/builds/dual-used-rtx-3090-70b/) · [Everything this machine runs](https://whatcanirunai.vercel.app/what-can-i-run/rtx-3090/) · [Q4 vs Q8 vs FP16](https://whatcanirunai.vercel.app/quantization/)

## Memory at each quant

| Quant    | Need vs 48.0 GB | Verdict       |
| -------- | --------------- | ------------- |
| Q4\_K\_M | 78.0 GB         | Q4Won’t fit   |
| Q8\_0    | 136.0 GB        | Q8Won’t fit   |
| FP16     | 255.1 GB        | FP16Won’t fit |

## Estimated tokens per second

No speed estimate: GLM-4.5-Air does not fit in the 48.0 GB this machine can use, so we do not publish a tokens/sec figure for CPU offload.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 GLM-4.5-Air on dual RTX 3090s at Q4\_K\_M__
| Context | KV cache (FP16) | Total at Q4\_K\_M | Verdict   |
| ------- | --------------- | ----------------- | --------- |
| 4k      | 0.8 GB          | 78.0 GB           | Won’t fit |
| 8k      | 1.5 GB          | 78.8 GB           | Won’t fit |
| 16k     | 3.1 GB          | 80.3 GB           | Won’t fit |
| 32k     | 6.2 GB          | 83.4 GB           | Won’t fit |
| 64k     | 12.3 GB         | 89.6 GB           | Won’t fit |
| 128k    | 24.7 GB         | 101.9 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

* 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 GLM-4.5-Air at Q4, you need about 78.0 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).
* CPU offload can load the weights anyway, but it is slow. We do not publish an offload tokens/sec figure.
* Full hardware list: [GLM-4.5-Air](https://whatcanirunai.vercel.app/models/glm-4.5-air/). Specs for this machine: [GeForce RTX 3090 (used as a pair)](https://whatcanirunai.vercel.app/hardware/rtx-3090/).

## 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 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 a Mac Studio 256GB](https://whatcanirunai.vercel.app/can-i-run/glm-4.5-air/mac-studio-m5-ultra-256/)Fits
* [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 dual RTX 3090s

* [Llama 3.3 70B on dual RTX 3090s](https://whatcanirunai.vercel.app/can-i-run/llama-3.3-70b-instruct/dual-rtx-3090/)Won’t fit
* [R1 Distill 70B on dual RTX 3090s](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1-distill-llama-70b/dual-rtx-3090/)Won’t fit
* [gpt-oss-120b on dual RTX 3090s](https://whatcanirunai.vercel.app/can-i-run/gpt-oss-120b/dual-rtx-3090/)Won’t fit
* [Qwen3-235B on dual RTX 3090s](https://whatcanirunai.vercel.app/can-i-run/qwen3-235b-a22b/dual-rtx-3090/)Won’t fit
* [DeepSeek-R1 on dual RTX 3090s](https://whatcanirunai.vercel.app/can-i-run/deepseek-r1/dual-rtx-3090/)Won’t fit
* [Mixtral 8x22B on dual RTX 3090s](https://whatcanirunai.vercel.app/can-i-run/mixtral-8x22b-instruct-v0.1/dual-rtx-3090/)Won’t fit

## Quick answers

Can I run GLM-4.5-Air on dual RTX 3090s?

No. GLM-4.5-Air at Q4\_K\_M needs about 78.0 GB, and dual RTX 3090s have 48.0 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 dual RTX 3090s?

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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