text model · GLM · iOS
Can I run GLM-4.6 on iPhone 16 Pro?
No. GLM-4.6 needs ~221.3 GB even at Q4_K_M, but iPhone 16 Pro only has ~4.5 GB usable.
Needs ~221.3 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 216.8 GB: GLM-4.6 needs roughly 221.3 GB at Q4_K_M and iPhone 16 Pro leaves only about 4.5 GB usable for a model. No single tracked device has enough memory; GLM-4.6 needs a multi-GPU or high-memory rig.
- Q4_K_M needed
- ~221.3 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
- 357B (MoE, 32B active)
- Q4_K_M size
- 216 GB
- Q8_0 size
- 379 GB
- Context
- 200k
- Memory
- 8 GB unified
- Usable for weights
- ~4.5 GB
- Power draw
- ~12 W
- Best runtime
- llama.cpp + Metal (via PocketPal or Off Grid app)
What you can run instead
FAQ
Can iPhone 16 Pro run GLM-4.6?
No. GLM-4.6 needs ~221.3 GB even at Q4_K_M, but iPhone 16 Pro only has ~4.5 GB usable.
How much memory does GLM-4.6 need?
iPhone 16 Pro does not have enough memory. At Q4_K_M the weights are ~216 GB; with KV cache and runtime overhead, budget ~221.3 GB at a 4k context. It is a Mixture-of-Experts model (357B total / 32B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run GLM-4.6 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
A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/glm-4.6/iphone-16-pro) Sources
- abachy.com
- apple.com/iphone-16-pro
- apple.com/newsroom
- developer.apple.com
- en.wikipedia.org
- enclaveai.app
- github.com/a-ghorbani
- github.com/google-ai-edge
- github.com/mainframecomputer
- github.com/ollama
- gsmarena.com
- huggingface.co/unsloth
- huggingface.co/zai-org
- layla-network.ai
- macrumors.com
- privatellm.app
- unsloth.ai
- versus.com
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.