text model · DeepSeek-V3 · iOS
Can I run DeepSeek V3 on iPad Pro M4 (16GB, 1TB/2TB config)?
No. DeepSeek V3 needs ~383.7 GB even at Q4_K_M, but iPad Pro M4 (16GB, 1TB/2TB config) only has ~12 GB usable.
Needs ~383.7 GB even at Q4_K_M, but only ~12 GB is usable.
- Q4_K_M needed
- ~383.7 GB
- Usable on device
- ~12 GB
- Device memory
- 16 GB
How to run it
On iOS use Apple Foundation Models (Built into iOS 26, ~3B on-device model, zero download, fully private.). Phones realistically run 1B-4B class models. Anything larger thermally throttles or OOMs.
- Parameters
- 671B (MoE, 37B active)
- Q4_K_M size
- 376.66 GB
- Context
- 128k
- Ollama tag
- deepseek-v3:671b
- Memory
- 16 GB unified
- Usable for weights
- ~12 GB
- Best runtime
- MLX (via Python or Swift; mlx-lm package)
What you can run instead
FAQ
Can iPad Pro M4 (16GB, 1TB/2TB config) run DeepSeek V3?
No. DeepSeek V3 needs ~383.7 GB even at Q4_K_M, but iPad Pro M4 (16GB, 1TB/2TB config) only has ~12 GB usable.
How much memory does DeepSeek V3 need?
iPad Pro M4 (16GB, 1TB/2TB config) does not have enough memory. At Q4_K_M the weights are ~376.66 GB; with KV cache and runtime overhead, budget ~383.7 GB at a 4k context. It is a Mixture-of-Experts model (671B total / 37B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run DeepSeek V3 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.
Sources
Memory figures are estimates. See methodology.