text model · gpt-oss · Android

Can I run gpt-oss 120B on Samsung Galaxy S25 Ultra (16GB, 1TB config only)?

Compatibility verdict VRAM threshold engine

No, not enough memory would not load

No. gpt-oss 120B needs ~62.4 GB even at Q4_K_M, but Samsung Galaxy S25 Ultra (16GB, 1TB config only) only has ~12 GB usable.

Needs ~62.4 GB Device usable ~12 GB

Needs ~62.4 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 50.4 GB: gpt-oss 120B needs roughly 62.4 GB at Q4_K_M and Samsung Galaxy S25 Ultra (16GB, 1TB config only) leaves only about 12 GB usable for a model. The lightest tracked hardware that runs gpt-oss 120B is the Apple M4 Max (128GB) at 128 GB. See gpt-oss 120B on Apple M4 Max (128GB).

Q4_K_M needed: ~62.4 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

~52.4 GB

Q3_K_M

~60.6 GB

Q4_K_M

~62.4 GB

Q5_K_M

~86.8 GB

Q6_K

~99.3 GB

Q8_0

~4.2 GB

FP16

~237.4 GB

The line marks Samsung Galaxy S25 Ultra (16GB, 1TB config only)'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 gpt-oss

Parameters: 117B (MoE, 5.1B active)
Q4_K_M size: 59.03 GB
Q8_0 size: 0.79 GB
Context: 128k
Ollama tag: gpt-oss:120b

Full gpt-oss 120B requirements →

Device Android

Memory: 16 GB ram
Usable for weights: ~12 GB
Power draw: ~8 W
Best runtime: llama.cpp (PocketPal) or MLC-LLM (Adreno GPU path)

Best models for Samsung Galaxy S25 Ultra (16GB, 1TB config only) →

What you can run instead

Run gpt-oss 120B on other hardware

Apple M4 Max (128GB)AMD Ryzen AI Halo (128GB)Apple M5 Max (128GB)Apple M3 Ultra (256GB)

FAQ

Can Samsung Galaxy S25 Ultra (16GB, 1TB config only) run gpt-oss 120B?

No. gpt-oss 120B needs ~62.4 GB even at Q4_K_M, but Samsung Galaxy S25 Ultra (16GB, 1TB config only) only has ~12 GB usable.

How much memory does gpt-oss 120B need?

Samsung Galaxy S25 Ultra (16GB, 1TB config only) does not have enough memory. At Q4_K_M the weights are ~59.03 GB; with KV cache and runtime overhead, budget ~62.4 GB at a 4k context. It is a Mixture-of-Experts model (117B total / 5.1B active), so all experts must stay in memory; memory tracks total params, not active params.

What is the best tool to run gpt-oss 120B 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

[![gpt-oss 120B on Samsung Galaxy S25 Ultra (16GB, 1TB config only)](https://localmodel.run/badge/gpt-oss-120b/samsung-s25-ultra-16gb.svg)](https://localmodel.run/can-i-run/gpt-oss-120b/samsung-s25-ultra-16gb)

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.