text model · DeepSeek-V2 · iOS
Can I run DeepSeek-V2-Lite on iPhone 16?
No. DeepSeek-V2-Lite needs ~12.2 GB even at Q4_K_M, but iPhone 16 only has ~4.5 GB usable.
Needs ~12.2 GB even at Q4_K_M, but only ~4.5 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 7.7 GB: DeepSeek-V2-Lite needs roughly 12.2 GB at Q4_K_M and iPhone 16 leaves only about 4.5 GB usable for a model. The lightest tracked hardware that runs DeepSeek-V2-Lite is the Nvidia GeForce RTX 4060 Ti (16GB) at 16 GB. See DeepSeek-V2-Lite on Nvidia GeForce RTX 4060 Ti (16GB).
- Q4_K_M needed
- ~12.2 GB
- Usable on device
- ~4.5 GB
- Device memory
- 8 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
- 16B (MoE, 2.4B active)
- Q4_K_M size
- 10.4 GB
- Q8_0 size
- 16.8 GB
- Context
- 32k
- Ollama tag
- deepseek-v2:16b
- Memory
- 8 GB unified
- Usable for weights
- ~4.5 GB
- Power draw
- ~11 W
- Best runtime
- llama.cpp + Metal (via PocketPal or Off Grid app)
What you can run instead
Run DeepSeek-V2-Lite on other hardware
FAQ
Can iPhone 16 run DeepSeek-V2-Lite?
No. DeepSeek-V2-Lite needs ~12.2 GB even at Q4_K_M, but iPhone 16 only has ~4.5 GB usable.
How much memory does DeepSeek-V2-Lite need?
iPhone 16 does not have enough memory. At Q4_K_M the weights are ~10.4 GB; with KV cache and runtime overhead, budget ~12.2 GB at a 4k context. It is a Mixture-of-Experts model (16B total / 2.4B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run DeepSeek-V2-Lite 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-v2-lite/iphone-16) Sources
- apple.com
- developer.apple.com
- en.wikipedia.org/wiki/Apple_A18
- en.wikipedia.org/wiki/IPhone_16
- enclaveai.app
- github.com/a-ghorbani
- github.com/deepseek-ai
- github.com/google-ai-edge
- github.com/mainframecomputer
- gsmarena.com
- huggingface.co/deepseek-ai
- huggingface.co/mradermacher
- layla-network.ai
- macrumors.com
- ollama.com
- privatellm.app
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.