text model · Nemotron · Android
Can I run Nemotron Nano 9B v2 on Generic Android Phone (8GB RAM)?
No. Nemotron Nano 9B v2 needs ~7.6 GB even at Q4_K_M, but Generic Android Phone (8GB RAM) only has ~4.5 GB usable.
Needs ~7.6 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 3.1 GB: Nemotron Nano 9B v2 needs roughly 7.6 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 Nemotron Nano 9B v2 is the Nvidia GeForce RTX 3060 (12GB) at 12 GB. See Nemotron Nano 9B v2 on Nvidia GeForce RTX 3060 (12GB).
- Q4_K_M needed
- ~7.6 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
- 9B
- Q4_K_M size
- 6.08 GB
- Q8_0 size
- 8.81 GB
- Context
- 128k
- Memory
- 8 GB ram
- Usable for weights
- ~4.5 GB
- Best runtime
- llama.cpp (PocketPal or SmolChat)
What you can run instead
Run Nemotron Nano 9B v2 on other hardware
FAQ
Can Generic Android Phone (8GB RAM) run Nemotron Nano 9B v2?
No. Nemotron Nano 9B v2 needs ~7.6 GB even at Q4_K_M, but Generic Android Phone (8GB RAM) only has ~4.5 GB usable.
How much memory does Nemotron Nano 9B v2 need?
Generic Android Phone (8GB RAM) does not have enough memory. At Q4_K_M the weights are ~6.08 GB; with KV cache and runtime overhead, budget ~7.6 GB at a 4k context.
What is the best tool to run Nemotron Nano 9B v2 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/nemotron-nano-9b/android-generic-8gb) Sources
- github.com/a-ghorbani
- github.com/google-ai-edge
- github.com/mlc-ai
- github.com/shubham0204
- github.com/Vali-98
- huggingface.co/bartowski/Llama-3.2-1B-Instruct-GGUF
- huggingface.co/bartowski/nvidia_NVIDIA-Nemotron-Nano-9B-v2-GGUF
- huggingface.co/nvidia/NVIDIA-Nemotron-Nano-9B-v2
- huggingface.co/nvidia/NVIDIA-Nemotron-Nano-9B-v2/discussions
- layla-network.ai
- ollama.com/library/llama3.2:1b
- ollama.com/library/nemotron-3-nano
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.