text model · GLM · iOS

Can I run GLM-4-32B-0414 on iPhone 16?

Compatibility verdict VRAM threshold engine

No, not enough memory would not load

No. GLM-4-32B-0414 needs ~20.5 GB even at Q4_K_M, but iPhone 16 only has ~4.5 GB usable.

Needs ~20.5 GB Device usable ~4.5 GB

Needs ~20.5 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 16 GB: GLM-4-32B-0414 needs roughly 20.5 GB at Q4_K_M and iPhone 16 leaves only about 4.5 GB usable for a model. The lightest tracked hardware that runs GLM-4-32B-0414 is the Nvidia GeForce RTX 4090 (24GB) at 24 GB. See GLM-4-32B-0414 on Nvidia GeForce RTX 4090 (24GB).

Q4_K_M needed: ~20.5 GB
Usable on device: ~4.5 GB
Device memory: 8 GB

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Which quant fits

Quant ladder vs ~4.5 GB usable

Q2_K

~15.6 GB

Q3_K_M

~17.8 GB

Q4_K_M

~20.5 GB

Q5_K_M

~25 GB

Q6_K

~28.4 GB

Q8_0

~34.4 GB

FP16

~67.3 GB

The line marks iPhone 16's ~4.5 GB budget; rungs past it are too large.

How to run it

On iOS use Apple Foundation Models (Built into iOS 26, ~3B on-device model, zero download, fully private.).

Model GLM

Parameters: 32B
Q4_K_M size: 18.33 GB
Q8_0 size: 32.24 GB
Context: 32k

Full GLM-4-32B-0414 requirements →

Device iOS

Memory: 8 GB unified
Usable for weights: ~4.5 GB
Power draw: ~11 W
Best runtime: llama.cpp + Metal (via PocketPal or Off Grid app)

Best models for iPhone 16 →

What you can run instead

Run GLM-4-32B-0414 on other hardware

Nvidia GeForce RTX 4090 (24GB)Nvidia GeForce RTX 3090 (24GB)AMD Radeon RX 7900 XTX (24GB)Nvidia GeForce RTX 5090 (32GB)32GB RAM Laptop (CPU/iGPU only)Apple M5 (32GB)

FAQ

Can iPhone 16 run GLM-4-32B-0414?

No. GLM-4-32B-0414 needs ~20.5 GB even at Q4_K_M, but iPhone 16 only has ~4.5 GB usable.

How much memory does GLM-4-32B-0414 need?

iPhone 16 does not have enough memory. At Q4_K_M the weights are ~18.33 GB; with KV cache and runtime overhead, budget ~20.5 GB at a 4k context.

What is the best tool to run GLM-4-32B-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.

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Markdown

[![GLM-4-32B-0414 on iPhone 16](https://localmodel.run/badge/glm-4-32b-0414/iphone-16.svg)](https://localmodel.run/can-i-run/glm-4-32b-0414/iphone-16)

Sources

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.