text model · DeepSeek-V2 · Android
Can I run DeepSeek-V2-Lite on Generic Android Phone (8GB RAM)?
No. DeepSeek-V2-Lite needs ~12.2 GB even at Q4_K_M, but Generic Android Phone (8GB RAM) 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 Android use PocketPal AI (Polished app, download GGUF and run offline.). NPU acceleration is limited and chip-specific; most apps run on CPU. Expect 1B-4B class.
- 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 ram
- Usable for weights
- ~4.5 GB
- Best runtime
- llama.cpp (PocketPal or SmolChat)
What you can run instead
Run DeepSeek-V2-Lite on other hardware
FAQ
Can Generic Android Phone (8GB RAM) run DeepSeek-V2-Lite?
No. DeepSeek-V2-Lite needs ~12.2 GB even at Q4_K_M, but Generic Android Phone (8GB RAM) only has ~4.5 GB usable.
How much memory does DeepSeek-V2-Lite need?
Generic Android Phone (8GB RAM) 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 Android?
On Android, PocketPal AI (Polished app, download GGUF and run offline.) is the go-to option. NPU acceleration is limited and chip-specific; most apps run on CPU. Expect 1B-4B class.
Sources
Memory figures are estimates. See methodology.