text model · Nemotron 3 · Android
Can I run Nemotron Cascade 2 30B-A3B on Samsung Galaxy S24 Ultra?
No. Nemotron Cascade 2 30B-A3B needs ~25.1 GB even at Q4_K_M, but Samsung Galaxy S24 Ultra only has ~8.5 GB usable.
Needs ~25.1 GB even at Q4_K_M, but only ~8.5 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 16.6 GB: Nemotron Cascade 2 30B-A3B needs roughly 25.1 GB at Q4_K_M and Samsung Galaxy S24 Ultra leaves only about 8.5 GB usable for a model. The lightest tracked hardware that runs Nemotron Cascade 2 30B-A3B is the Nvidia GeForce RTX 5090 (32GB) at 32 GB. See Nemotron Cascade 2 30B-A3B on Nvidia GeForce RTX 5090 (32GB).
- Q4_K_M needed
- ~25.1 GB
- Usable on device
- ~8.5 GB
- Device memory
- 12 GB
Which quant fits
How to run it
On Android use PocketPal AI (Polished app, download GGUF and run offline.).
- Parameters
- 30B (MoE, 3B active)
- Q4_K_M size
- 23.03 GB
- Q8_0 size
- 31.28 GB
- Context
- 256k
- Ollama tag
- nemotron-cascade-2:30b
- Memory
- 12 GB ram
- Usable for weights
- ~8.5 GB
- Power draw
- ~8 W
- Best runtime
- llama.cpp (PocketPal) or MLC-LLM (Adreno GPU path)
What you can run instead
Run Nemotron Cascade 2 30B-A3B on other hardware
FAQ
Can Samsung Galaxy S24 Ultra run Nemotron Cascade 2 30B-A3B?
No. Nemotron Cascade 2 30B-A3B needs ~25.1 GB even at Q4_K_M, but Samsung Galaxy S24 Ultra only has ~8.5 GB usable.
How much memory does Nemotron Cascade 2 30B-A3B need?
Samsung Galaxy S24 Ultra does not have enough memory. At Q4_K_M the weights are ~23.03 GB; with KV cache and runtime overhead, budget ~25.1 GB at a 4k context. It is a Mixture-of-Experts model (30B total / 3B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run Nemotron Cascade 2 30B-A3B 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-cascade-2-30b-a3b/samsung-s24-ultra) Sources
- androidauthority.com
- comparigon.com
- github.com/a-ghorbani
- github.com/google-ai-edge
- github.com/mlc-ai
- github.com/shubham0204
- github.com/Vali-98
- gsmarena.com/samsung_galaxy_s24_ultra-12419.php
- gsmarena.com/samsung_galaxy_s24_ultra-12771.php
- gsmarena.com/samsung_galaxy_s24_ultra-review-2754p5.php
- huggingface.co/bartowski/Meta-Llama-3.1-8B-Instruct-GGUF
- huggingface.co/bartowski/nvidia_Nemotron-Cascade-2-30B-A3B-GGUF
- huggingface.co/nvidia
- layla-network.ai
- ollama.com
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.