text model · DeepSeek-R1 · iOS
Can I run DeepSeek-R1-0528 on iPhone 17 Pro?
No. DeepSeek-R1-0528 needs ~384.1 GB even at Q4_K_M, but iPhone 17 Pro only has ~8 GB usable.
Needs ~384.1 GB even at Q4_K_M, but only ~8 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 376.1 GB: DeepSeek-R1-0528 needs roughly 384.1 GB at Q4_K_M and iPhone 17 Pro leaves only about 8 GB usable for a model. No single tracked device has enough memory; DeepSeek-R1-0528 needs a multi-GPU or high-memory rig.
- Q4_K_M needed
- ~384.1 GB
- Usable on device
- ~8 GB
- Device memory
- 12 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
- 671B (MoE, 37B active)
- Q4_K_M size
- 377.13 GB
- Q8_0 size
- 664.3 GB
- Context
- 160k
- Ollama tag
- deepseek-r1:671b
- Memory
- 12 GB unified
- Usable for weights
- ~8 GB
- Power draw
- ~12 W
- Best runtime
- llama.cpp + Metal (via PocketPal or Off Grid app)
What you can run instead
FAQ
Can iPhone 17 Pro run DeepSeek-R1-0528?
No. DeepSeek-R1-0528 needs ~384.1 GB even at Q4_K_M, but iPhone 17 Pro only has ~8 GB usable.
How much memory does DeepSeek-R1-0528 need?
iPhone 17 Pro does not have enough memory. At Q4_K_M the weights are ~377.13 GB; with KV cache and runtime overhead, budget ~384.1 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-R1-0528 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/deepseek-r1-0528/iphone-17-pro) Sources
- aider.chat
- apple.com
- developer.apple.com
- en.wikipedia.org
- enclaveai.app
- github.com/a-ghorbani
- github.com/google-ai-edge
- github.com/mainframecomputer
- huggingface.co/deepseek-ai/DeepSeek-R1-0528
- huggingface.co/deepseek-ai/DeepSeek-R1-0528/blob
- huggingface.co/unsloth/DeepSeek-R1-0528-GGUF/tree/main/BF16
- huggingface.co/unsloth/DeepSeek-R1-0528-GGUF/tree/main/Q4_K_M
- huggingface.co/unsloth/DeepSeek-R1-0528-GGUF/tree/main/Q8_0
- layla-network.ai
- macrumors.com
- ollama.com
- openrouter.ai
- privatellm.app
- wccftech.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.