text model · DeepSeek-R1-Distill · Android
Can I run DeepSeek-R1-Distill-Llama 8B on Generic Android Phone (12GB RAM)?
Yes. DeepSeek-R1-Distill-Llama 8B runs on Generic Android Phone (12GB RAM) at Q4_K_M (~6.4 GB of ~8.5 GB usable).
Runs at Q4_K_M using ~6.4 GB of ~8.5 GB usable.
That figure is at a 4k context and moves about ±15% as context length changes. Generic Android Phone (12GB RAM) leaves ~2.1 GB of headroom.
- Q4_K_M needed
- ~6.4 GB
- Usable on device
- ~8.5 GB
- Device memory
- 12 GB
- Best quant
- Q4_K_M
Which quant fits
How to run it
On Android use PocketPal AI (Polished app, download GGUF and run offline.).
- Parameters
- 8B
- Q4_K_M size
- 4.92 GB
- Q8_0 size
- 8.54 GB
- Context
- 128k
- Ollama tag
- deepseek-r1:8b
- Memory
- 12 GB ram
- Usable for weights
- ~8.5 GB
- Best runtime
- llama.cpp (PocketPal) or MLC-LLM
You could also run
Run DeepSeek-R1-Distill-Llama 8B on other hardware
FAQ
Can Generic Android Phone (12GB RAM) run DeepSeek-R1-Distill-Llama 8B?
Yes. DeepSeek-R1-Distill-Llama 8B runs on Generic Android Phone (12GB RAM) at Q4_K_M (~6.4 GB of ~8.5 GB usable).
How much memory does DeepSeek-R1-Distill-Llama 8B need?
Generic Android Phone (12GB RAM) has room to spare. At Q4_K_M the weights are ~4.92 GB; with KV cache and runtime overhead, budget ~6.4 GB at a 4k context.
What is the best tool to run DeepSeek-R1-Distill-Llama 8B 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.
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A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/deepseek-r1-distill-llama-8b/android-generic-12gb) Sources
- github.com/a-ghorbani
- github.com/deepseek-ai
- github.com/google-ai-edge
- github.com/mlc-ai
- github.com/shubham0204
- github.com/Vali-98
- huggingface.co/bartowski/DeepSeek-R1-Distill-Llama-8B-GGUF
- huggingface.co/bartowski/Meta-Llama-3.1-8B-Instruct-GGUF
- layla-network.ai
- ollama.com/library/deepseek-r1
- ollama.com/library/llama3.1:8b
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.