text model · OLMo · iOS
Can I run OLMo 2 32B Instruct on iPad Pro M4 (16GB, 1TB/2TB config)?
No. OLMo 2 32B Instruct needs ~21.7 GB even at Q4_K_M, but iPad Pro M4 (16GB, 1TB/2TB config) only has ~12 GB usable.
Needs ~21.7 GB even at Q4_K_M, but only ~12 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 9.7 GB: OLMo 2 32B Instruct needs roughly 21.7 GB at Q4_K_M and iPad Pro M4 (16GB, 1TB/2TB config) leaves only about 12 GB usable for a model. The lightest tracked hardware that runs OLMo 2 32B Instruct is the Nvidia GeForce RTX 4090 (24GB) at 24 GB. See OLMo 2 32B Instruct on Nvidia GeForce RTX 4090 (24GB).
- Q4_K_M needed
- ~21.7 GB
- Usable on device
- ~12 GB
- Device memory
- 16 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
- 32B
- Q4_K_M size
- 19.5 GB
- Q8_0 size
- 34.3 GB
- Context
- 4k
- Memory
- 16 GB unified
- Usable for weights
- ~12 GB
- Power draw
- ~14 W
- Best runtime
- MLX (via Python or Swift; mlx-lm package)
What you can run instead
Run OLMo 2 32B Instruct on other hardware
FAQ
Can iPad Pro M4 (16GB, 1TB/2TB config) run OLMo 2 32B Instruct?
No. OLMo 2 32B Instruct needs ~21.7 GB even at Q4_K_M, but iPad Pro M4 (16GB, 1TB/2TB config) only has ~12 GB usable.
How much memory does OLMo 2 32B Instruct need?
iPad Pro M4 (16GB, 1TB/2TB config) does not have enough memory. At Q4_K_M the weights are ~19.5 GB; with KV cache and runtime overhead, budget ~21.7 GB at a 4k context.
What is the best tool to run OLMo 2 32B Instruct 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/olmo-2-32b/ipad-pro-m4-16gb) Sources
- apple.com/ipad-pro
- apple.com/newsroom
- developer.apple.com
- enclaveai.app
- github.com/a-ghorbani
- github.com/google-ai-edge
- github.com/mainframecomputer
- gsmarena.com
- huggingface.co/allenai/OLMo-2-0325-32B
- huggingface.co/allenai/OLMo-2-0325-32B-Instruct
- huggingface.co/allenai/OLMo-2-0325-32B-Instruct-GGUF
- huggingface.co/unsloth
- layla-network.ai
- ollama.com
- phonearena.com
- privatellm.app
- support.apple.com/en-us/119891
- support.apple.com/en-us/119892
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.