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text model · Yi · Android

Can I run Yi 1.5 34B on Samsung Galaxy S25 Ultra (16GB, 1TB config only)?

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

No. Yi 1.5 34B needs ~21.4 GB even at Q4_K_M, but Samsung Galaxy S25 Ultra (16GB, 1TB config only) only has ~12 GB usable.

Needs ~21.4 GB Device usable ~12 GB

Needs ~21.4 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 9.4 GB: Yi 1.5 34B needs roughly 21.4 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 Yi 1.5 34B is the Nvidia GeForce RTX 4090 (24GB) at 24 GB. See Yi 1.5 34B on Nvidia GeForce RTX 4090 (24GB).

Q4_K_M needed
~21.4 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
~16.4 GB
Q3_K_M
~18.8 GB
Q4_K_M
~21.4 GB
Q5_K_M
~26.4 GB
Q6_K
~30.1 GB
Q8_0
~36.2 GB
FP16
~70.2 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 Yi
Parameters
34B
Q4_K_M size
19.24 GB
Q8_0 size
34.03 GB
Context
32k
Full Yi 1.5 34B 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 Yi 1.5 34B on other hardware

FAQ

Can Samsung Galaxy S25 Ultra (16GB, 1TB config only) run Yi 1.5 34B?

No. Yi 1.5 34B needs ~21.4 GB even at Q4_K_M, but Samsung Galaxy S25 Ultra (16GB, 1TB config only) only has ~12 GB usable.

How much memory does Yi 1.5 34B need?

Samsung Galaxy S25 Ultra (16GB, 1TB config only) does not have enough memory. At Q4_K_M the weights are ~19.24 GB; with KV cache and runtime overhead, budget ~21.4 GB at a 4k context.

What is the best tool to run Yi 1.5 34B 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.