text model · RNJ · Android
Can I run RNJ-1 8B on Generic Android Phone (8GB RAM)?
No. RNJ-1 8B needs ~6.3 GB even at Q4_K_M, but Generic Android Phone (8GB RAM) only has ~4.5 GB usable.
Needs ~6.3 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 1.8 GB: RNJ-1 8B needs roughly 6.3 GB at Q4_K_M and Generic Android Phone (8GB RAM) leaves only about 4.5 GB usable for a model. The lightest tracked hardware that runs RNJ-1 8B is the Nvidia GeForce RTX 3060 Ti (8GB) at 8 GB. See RNJ-1 8B on Nvidia GeForce RTX 3060 Ti (8GB).
- Q4_K_M needed
- ~6.3 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
- 8B
- Q4_K_M size
- 4.76 GB
- Q8_0 size
- 8.23 GB
- Context
- 32k
- Ollama tag
- rnj-1:8b
- Memory
- 8 GB ram
- Usable for weights
- ~4.5 GB
- Best runtime
- llama.cpp (PocketPal or SmolChat)
What you can run instead
Run RNJ-1 8B on other hardware
FAQ
Can Generic Android Phone (8GB RAM) run RNJ-1 8B?
No. RNJ-1 8B needs ~6.3 GB even at Q4_K_M, but Generic Android Phone (8GB RAM) only has ~4.5 GB usable.
How much memory does RNJ-1 8B need?
Generic Android Phone (8GB RAM) does not have enough memory. At Q4_K_M the weights are ~4.76 GB; with KV cache and runtime overhead, budget ~6.3 GB at a 4k context.
What is the best tool to run RNJ-1 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/rnj-1-8b/android-generic-8gb) Sources
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.