text model · DeepSeek-R1-Distill · iOS
Can I run DeepSeek-R1-0528-Qwen3-8B on iPhone 17 Pro?
Yes. DeepSeek-R1-0528-Qwen3-8B runs on iPhone 17 Pro at Q4_K_M (~6.2 GB of ~8 GB usable).
Runs at Q4_K_M using ~6.2 GB of ~8 GB usable.
That figure is at a 4k context and moves about ±15% as context length changes. iPhone 17 Pro leaves ~1.8 GB of headroom.
- Q4_K_M needed
- ~6.2 GB
- Usable on device
- ~8 GB
- Device memory
- 12 GB
- Best quant
- Q4_K_M
Which quant fits
Running cost · estimate
- Power draw
- ~12 W
- Electricity / 1M tokens
- ~$0.06
- Pays for itself after
- ~2,498M tok
At ~$0.15/kWh and the estimated ~8 tok/s, a million generated tokens costs about $0.06 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$1,099 iPhone 17 Pro pays for itself after roughly 2,498 million tokens, so local hardware is mostly a fixed cost, not a per-token one. TDP is the peak draw, so this is an upper bound. Assumptions.
How to run it
On iOS use Apple Foundation Models (Built into iOS 26, ~3B on-device model, zero download, fully private.).
- Parameters
- 8.19B
- Q4_K_M size
- 4.68 GB
- Q8_0 size
- 8.11 GB
- Context
- 128k
- Ollama tag
- deepseek-r1:8b-0528-qwen3-q4_K_M
- Memory
- 12 GB unified
- Usable for weights
- ~8 GB
- Power draw
- ~12 W
- Best runtime
- llama.cpp + Metal (via PocketPal or Off Grid app)
You could also run
Run DeepSeek-R1-0528-Qwen3-8B on other hardware
FAQ
Can iPhone 17 Pro run DeepSeek-R1-0528-Qwen3-8B?
Yes. DeepSeek-R1-0528-Qwen3-8B runs on iPhone 17 Pro at Q4_K_M (~6.2 GB of ~8 GB usable).
How much memory does DeepSeek-R1-0528-Qwen3-8B need?
iPhone 17 Pro has room to spare. At Q4_K_M the weights are ~4.68 GB; with KV cache and runtime overhead, budget ~6.2 GB at a 4k context.
What is the best tool to run DeepSeek-R1-0528-Qwen3-8B 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.
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-qwen3-8b/iphone-17-pro) Sources
- 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-Qwen3-8B
- huggingface.co/deepseek-ai/DeepSeek-R1-0528-Qwen3-8B/blob
- huggingface.co/unsloth
- layla-network.ai
- macrumors.com
- ollama.com
- 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.