text model · GLM · Android
Can I run GLM-4.5-Air on Google Pixel 9 Pro?
No. GLM-4.5-Air needs ~71.8 GB even at Q4_K_M, but Google Pixel 9 Pro only has ~10.5 GB usable.
Needs ~71.8 GB even at Q4_K_M, but only ~10.5 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 61.3 GB: GLM-4.5-Air needs roughly 71.8 GB at Q4_K_M and Google Pixel 9 Pro leaves only about 10.5 GB usable for a model. The lightest tracked hardware that runs GLM-4.5-Air is the Apple M4 Max (128GB) at 128 GB. See GLM-4.5-Air on Apple M4 Max (128GB).
- Q4_K_M needed
- ~71.8 GB
- Usable on device
- ~10.5 GB
- Device memory
- 16 GB
Which quant fits
How to run it
On Android use PocketPal AI (Polished app, download GGUF and run offline.).
- Parameters
- 106B (MoE, 12B active)
- Q4_K_M size
- 68.45 GB
- Q8_0 size
- 109.39 GB
- Context
- 128k
- Memory
- 16 GB ram
- Usable for weights
- ~10.5 GB
- Best runtime
- llama.cpp (PocketPal) or MLC-LLM (Adreno GPU path)
What you can run instead
Run GLM-4.5-Air on other hardware
FAQ
Can Google Pixel 9 Pro run GLM-4.5-Air?
No. GLM-4.5-Air needs ~71.8 GB even at Q4_K_M, but Google Pixel 9 Pro only has ~10.5 GB usable.
How much memory does GLM-4.5-Air need?
Google Pixel 9 Pro does not have enough memory. At Q4_K_M the weights are ~68.45 GB; with KV cache and runtime overhead, budget ~71.8 GB at a 4k context. It is a Mixture-of-Experts model (106B total / 12B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run GLM-4.5-Air on Android?
On Android, PocketPal AI (Polished app, download GGUF and run offline.) is the go-to option. NPU acceleration is limited and chip-specific; most apps run on CPU. Expect 1B-4B class.
Embed this
A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/glm-4.5-air/pixel-9-pro) Sources
- 9to5google.com
- androidpolice.com
- artificialanalysis.ai
- comparigon.com
- docs.z.ai
- github.com/a-ghorbani
- github.com/google-ai-edge
- github.com/mlc-ai
- github.com/shubham0204
- github.com/Vali-98
- gsmarena.com/google_pixel_9_pro-12424.php
- gsmarena.com/google_pixel_9_pro-13218.php
- huggingface.co/bartowski
- huggingface.co/zai-org
- layla-network.ai
- llm-stats.com
- mlc.ai
Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.