text model · DeepSeek-R1 · Android
Can I run DeepSeek-R1-0528 on Generic Android Phone (8GB RAM)?
No. DeepSeek-R1-0528 needs ~384.1 GB even at Q4_K_M, but Generic Android Phone (8GB RAM) only has ~4.5 GB usable.
Needs ~384.1 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 379.6 GB: DeepSeek-R1-0528 needs roughly 384.1 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-R1-0528 needs a multi-GPU or high-memory rig.
- Q4_K_M needed
- ~384.1 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
- 377.13 GB
- Q8_0 size
- 664.3 GB
- Context
- 160k
- 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-R1-0528?
No. DeepSeek-R1-0528 needs ~384.1 GB even at Q4_K_M, but Generic Android Phone (8GB RAM) only has ~4.5 GB usable.
How much memory does DeepSeek-R1-0528 need?
Generic Android Phone (8GB RAM) does not have enough memory. At Q4_K_M the weights are ~377.13 GB; with KV cache and runtime overhead, budget ~384.1 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-R1-0528 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-r1-0528/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/DeepSeek-R1-0528
- huggingface.co/deepseek-ai/DeepSeek-R1-0528/blob
- huggingface.co/unsloth/DeepSeek-R1-0528-GGUF/tree/main/BF16
- huggingface.co/unsloth/DeepSeek-R1-0528-GGUF/tree/main/Q4_K_M
- huggingface.co/unsloth/DeepSeek-R1-0528-GGUF/tree/main/Q8_0
- layla-network.ai
- ollama.com/library/deepseek-r1:671b
- ollama.com/library/llama3.2:1b
- openrouter.ai
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.