text model · DeepSeek-R1-Distill · iOS
Can I run DeepSeek-R1-Distill-Llama 8B on iPad Pro M4 (16GB, 1TB/2TB config)?
Yes. DeepSeek-R1-Distill-Llama 8B runs on iPad Pro M4 (16GB, 1TB/2TB config) at Q4_K_M (~6.4 GB of ~12 GB usable).
Runs at Q4_K_M using ~6.4 GB of ~12 GB usable. You have room for Q8_0 for higher quality.
- Q4_K_M needed
- ~6.4 GB
- Usable on device
- ~12 GB
- Device memory
- 16 GB
- Best quant
- Q4_K_M
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
- 8B
- Q4_K_M size
- 4.92 GB
- Q8_0 size
- 8.54 GB
- Context
- 128k
- Ollama tag
- deepseek-r1:8b
- Memory
- 16 GB unified
- Usable for weights
- ~12 GB
- Best runtime
- MLX (via Python or Swift; mlx-lm package)
You could also run
Run DeepSeek-R1-Distill-Llama 8B on other hardware
FAQ
Can iPad Pro M4 (16GB, 1TB/2TB config) run DeepSeek-R1-Distill-Llama 8B?
Yes. DeepSeek-R1-Distill-Llama 8B runs on iPad Pro M4 (16GB, 1TB/2TB config) at Q4_K_M (~6.4 GB of ~12 GB usable).
How much memory does DeepSeek-R1-Distill-Llama 8B need?
iPad Pro M4 (16GB, 1TB/2TB config) has room to spare. At Q4_K_M the weights are ~4.92 GB; with KV cache and runtime overhead, budget ~6.4 GB at a 4k context.
What is the best tool to run DeepSeek-R1-Distill-Llama 8B 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.