text model · Nemotron · Android
Can I run Llama-3.3-Nemotron-Super-49B-v1 on Generic Android Phone (8GB RAM)?
No. Llama-3.3-Nemotron-Super-49B-v1 needs ~30.6 GB even at Q4_K_M, but Generic Android Phone (8GB RAM) only has ~4.5 GB usable.
Needs ~30.6 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 26.1 GB: Llama-3.3-Nemotron-Super-49B-v1 needs roughly 30.6 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 Llama-3.3-Nemotron-Super-49B-v1 is the Nvidia GeForce RTX 5090 (32GB) at 32 GB. See Llama-3.3-Nemotron-Super-49B-v1 on Nvidia GeForce RTX 5090 (32GB).
- Q4_K_M needed
- ~30.6 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
- 49B
- Q4_K_M size
- 28.14 GB
- Q8_0 size
- 49.36 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 Llama-3.3-Nemotron-Super-49B-v1 on other hardware
FAQ
Can Generic Android Phone (8GB RAM) run Llama-3.3-Nemotron-Super-49B-v1?
No. Llama-3.3-Nemotron-Super-49B-v1 needs ~30.6 GB even at Q4_K_M, but Generic Android Phone (8GB RAM) only has ~4.5 GB usable.
How much memory does Llama-3.3-Nemotron-Super-49B-v1 need?
Generic Android Phone (8GB RAM) does not have enough memory. At Q4_K_M the weights are ~28.14 GB; with KV cache and runtime overhead, budget ~30.6 GB at a 4k context.
What is the best tool to run Llama-3.3-Nemotron-Super-49B-v1 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-super-49b/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_Llama-3_3-Nemotron-Super-49B-v1-GGUF
- huggingface.co/nvidia
- layla-network.ai
- ollama.com/library/llama3.2:1b
- ollama.com/library/nemotron
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.