text model · DeepSeek-R1 · Android
Can I run DeepSeek R1 on Generic Android Phone (8GB RAM)?
No. DeepSeek R1 needs ~383.7 GB even at Q4_K_M, but Generic Android Phone (8GB RAM) only has ~4.5 GB usable.
Needs ~383.7 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 379.2 GB: DeepSeek R1 needs roughly 383.7 GB at Q4_K_M and Generic Android Phone (8GB RAM) leaves only about 4.5 GB usable for a model. No single tracked device has enough memory; DeepSeek R1 needs a multi-GPU or high-memory rig.
- Q4_K_M needed
- ~383.7 GB
- Usable on device
- ~4.5 GB
- Device memory
- 8 GB
Which quant fits
How to run it
On Android use PocketPal AI (Polished app, download GGUF and run offline.).
- Parameters
- 671B (MoE, 37B active)
- Q4_K_M size
- 376.65 GB
- Q8_0 size
- 664.3 GB
- Context
- 128k
- Ollama tag
- deepseek-r1:671b
- Memory
- 8 GB ram
- Usable for weights
- ~4.5 GB
- Best runtime
- llama.cpp (PocketPal or SmolChat)
What you can run instead
FAQ
Can Generic Android Phone (8GB RAM) run DeepSeek R1?
No. DeepSeek R1 needs ~383.7 GB even at Q4_K_M, but Generic Android Phone (8GB RAM) only has ~4.5 GB usable.
How much memory does DeepSeek R1 need?
Generic Android Phone (8GB RAM) does not have enough memory. At Q4_K_M the weights are ~376.65 GB; with KV cache and runtime overhead, budget ~383.7 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 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.
Embed this
A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/deepseek-r1/android-generic-8gb) Sources
- aider.chat
- github.com/a-ghorbani
- github.com/google-ai-edge
- github.com/mlc-ai
- github.com/shubham0204
- github.com/Vali-98
- huggingface.co/bartowski
- huggingface.co/deepseek-ai
- huggingface.co/unsloth
- layla-network.ai
- ollama.com/library/deepseek-r1
- ollama.com/library/deepseek-r1/tags
- ollama.com/library/llama3.2:1b
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.