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text model · Qwen2.5 · Android

Can I run Qwen2.5 72B on Samsung Galaxy S26 Ultra (16GB, 1TB config)?

Compatibility verdict VRAM threshold engine
No, not enough memory would not load

No. Qwen2.5 72B needs ~50.2 GB even at Q4_K_M, but Samsung Galaxy S26 Ultra (16GB, 1TB config) only has ~12 GB usable.

Needs ~50.2 GB Device usable ~12 GB

Needs ~50.2 GB even at Q4_K_M, but only ~12 GB is usable.

That figure is at a 4k context and moves about ±15% as context length changes.

The gap is about 38.2 GB: Qwen2.5 72B needs roughly 50.2 GB at Q4_K_M and Samsung Galaxy S26 Ultra (16GB, 1TB config) leaves only about 12 GB usable for a model. The lightest tracked hardware that runs Qwen2.5 72B is the Apple M4 Max (128GB) at 128 GB. See Qwen2.5 72B on Apple M4 Max (128GB).

Q4_K_M needed
~50.2 GB
Usable on device
~12 GB
Device memory
16 GB
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Which quant fits

Quant ladder vs ~12 GB usable
Q2_K
~33 GB
Q3_K_M
~38 GB
Q4_K_M
~50.2 GB
Q5_K_M
~54.1 GB
Q6_K
~61.8 GB
Q8_0
~80.1 GB
FP16
~146.8 GB
The line marks Samsung Galaxy S26 Ultra (16GB, 1TB config)'s ~12 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 Qwen2.5
Parameters
72B
Q4_K_M size
47.42 GB
Q8_0 size
77.26 GB
Context
128k
Ollama tag
qwen2.5:72b
Full Qwen2.5 72B requirements →
Device Android
Memory
16 GB ram
Usable for weights
~12 GB
Best runtime
llama.cpp (PocketPal) or MLC-LLM (Adreno GPU path)
Best models for Samsung Galaxy S26 Ultra (16GB, 1TB config) →

What you can run instead

Run Qwen2.5 72B on other hardware

FAQ

Can Samsung Galaxy S26 Ultra (16GB, 1TB config) run Qwen2.5 72B?

No. Qwen2.5 72B needs ~50.2 GB even at Q4_K_M, but Samsung Galaxy S26 Ultra (16GB, 1TB config) only has ~12 GB usable.

How much memory does Qwen2.5 72B need?

Samsung Galaxy S26 Ultra (16GB, 1TB config) does not have enough memory. At Q4_K_M the weights are ~47.42 GB; with KV cache and runtime overhead, budget ~50.2 GB at a 4k context.

What is the best tool to run Qwen2.5 72B 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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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.