text model · Qwen3 · Android

Can I run Qwen3 32B on Samsung Galaxy S24 Ultra?

Compatibility verdict VRAM threshold engine

No, not enough memory would not load

No. Qwen3 32B needs ~22 GB even at Q4_K_M, but Samsung Galaxy S24 Ultra only has ~8.5 GB usable.

Needs ~22 GB Device usable ~8.5 GB

Needs ~22 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 13.5 GB: Qwen3 32B needs roughly 22 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 Qwen3 32B is the Nvidia GeForce RTX 4090 (24GB) at 24 GB. See Qwen3 32B on Nvidia GeForce RTX 4090 (24GB).

Q4_K_M needed: ~22 GB
Usable on device: ~8.5 GB
Device memory: 12 GB

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Which quant fits

Quant ladder vs ~8.5 GB usable

Q2_K

~15.6 GB

Q3_K_M

~17.8 GB

Q4_K_M

~22 GB

Q5_K_M

~25 GB

Q6_K

~28.4 GB

Q8_0

~37 GB

FP16

~68.2 GB

The line marks Samsung Galaxy S24 Ultra's ~8.5 GB budget; rungs past it are too large.

How to run it

On Android use PocketPal AI (Polished app, download GGUF and run offline.).

Model Qwen3

Parameters: 32B
Q4_K_M size: 19.8 GB
Q8_0 size: 34.8 GB
Context: 32k
Ollama tag: qwen3:32b

Full Qwen3 32B requirements →

Device Android

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)

Best models for Samsung Galaxy S24 Ultra →

What you can run instead

Run Qwen3 32B on other hardware

Nvidia GeForce RTX 4090 (24GB)Nvidia GeForce RTX 3090 (24GB)AMD Radeon RX 7900 XTX (24GB)Nvidia GeForce RTX 5090 (32GB)32GB RAM Laptop (CPU/iGPU only)Apple M4 Pro (48GB)

FAQ

Can Samsung Galaxy S24 Ultra run Qwen3 32B?

No. Qwen3 32B needs ~22 GB even at Q4_K_M, but Samsung Galaxy S24 Ultra only has ~8.5 GB usable.

How much memory does Qwen3 32B need?

Samsung Galaxy S24 Ultra does not have enough memory. At Q4_K_M the weights are ~19.8 GB; with KV cache and runtime overhead, budget ~22 GB at a 4k context.

What is the best tool to run Qwen3 32B 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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Markdown

[![Qwen3 32B on Samsung Galaxy S24 Ultra](https://localmodel.run/badge/qwen3-32b/samsung-s24-ultra.svg)](https://localmodel.run/can-i-run/qwen3-32b/samsung-s24-ultra)

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.