text model · DeepSeek-V2 · iOS
Can I run DeepSeek-V2-Lite on iPhone 17?
No. DeepSeek-V2-Lite needs ~12.2 GB even at Q4_K_M, but iPhone 17 only has ~4.5 GB usable.
Needs ~12.2 GB even at Q4_K_M, but only ~4.5 GB is usable.
- Q4_K_M needed
- ~12.2 GB
- Usable on device
- ~4.5 GB
- Device memory
- 8 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
- 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
- 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 17 run DeepSeek-V2-Lite?
No. DeepSeek-V2-Lite needs ~12.2 GB even at Q4_K_M, but iPhone 17 only has ~4.5 GB usable.
How much memory does DeepSeek-V2-Lite need?
iPhone 17 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.
Sources
Memory figures are estimates. See methodology.