text model · GLM · iOS
Can I run GLM-4-9B-0414 on iPhone 17?
No. GLM-4-9B-0414 needs ~7.2 GB even at Q4_K_M, but iPhone 17 only has ~4.5 GB usable.
Needs ~7.2 GB even at Q4_K_M, but only ~4.5 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 2.7 GB: GLM-4-9B-0414 needs roughly 7.2 GB at Q4_K_M and iPhone 17 leaves only about 4.5 GB usable for a model. The lightest tracked hardware that runs GLM-4-9B-0414 is the Nvidia GeForce RTX 3060 (12GB) at 12 GB. See GLM-4-9B-0414 on Nvidia GeForce RTX 3060 (12GB).
- Q4_K_M needed
- ~7.2 GB
- Usable on device
- ~4.5 GB
- Device memory
- 8 GB
Which quant fits
How to run it
On iOS use Apple Foundation Models (Built into iOS 26, ~3B on-device model, zero download, fully private.).
- Parameters
- 9B
- Q4_K_M size
- 5.74 GB
- Q8_0 size
- 9.31 GB
- Context
- 32k
- Memory
- 8 GB unified
- Usable for weights
- ~4.5 GB
- Best runtime
- llama.cpp + Metal (via PocketPal or Off Grid app)
What you can run instead
Run GLM-4-9B-0414 on other hardware
FAQ
Can iPhone 17 run GLM-4-9B-0414?
No. GLM-4-9B-0414 needs ~7.2 GB even at Q4_K_M, but iPhone 17 only has ~4.5 GB usable.
How much memory does GLM-4-9B-0414 need?
iPhone 17 does not have enough memory. At Q4_K_M the weights are ~5.74 GB; with KV cache and runtime overhead, budget ~7.2 GB at a 4k context.
What is the best tool to run GLM-4-9B-0414 on iOS?
On iPhone and iPad, Apple Foundation Models (Built into iOS 26, ~3B on-device model, zero download, fully private.) is the standard choice. Phones realistically run 1B-4B class models. Anything larger thermally throttles or OOMs.
Embed this
[](https://localmodel.run/can-i-run/glm-4-9b-0414/iphone-17) Sources
- apple.com
- developer.apple.com
- en.wikipedia.org/wiki/Apple_A19
- en.wikipedia.org/wiki/IPhone_17
- enclaveai.app
- github.com/a-ghorbani
- github.com/google-ai-edge
- github.com/mainframecomputer
- github.com/zai-org
- huggingface.co/bartowski
- huggingface.co/THUDM
- huggingface.co/zai-org
- layla-network.ai
- macrumors.com
- privatellm.app
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.