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Can I run GLM-5.2 on Samsung Galaxy S26 Ultra (16GB, 1TB config)?

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

No. GLM-5.2 needs ~473.1 GB even at Q4_K_M, but Samsung Galaxy S26 Ultra (16GB, 1TB config) only has ~12 GB usable.

Needs ~473.1 GB Device usable ~12 GB

Needs ~473.1 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 461.1 GB: GLM-5.2 needs roughly 473.1 GB at Q4_K_M and Samsung Galaxy S26 Ultra (16GB, 1TB config) leaves only about 12 GB usable for a model. No single tracked device has enough memory; GLM-5.2 needs a multi-GPU or high-memory rig.

Q4_K_M needed
~473.1 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
~318.9 GB
Q3_K_M
~370.9 GB
Q4_K_M
~473.1 GB
Q5_K_M
~537.4 GB
Q6_K
~617.4 GB
Q8_0
~808.7 GB
FP16
~1515.3 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 GLM
Parameters
744B (MoE, 40B active)
Q4_K_M size
465.83 GB
Q8_0 size
801.36 GB
Context
1000k
Full GLM-5.2 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

FAQ

Can Samsung Galaxy S26 Ultra (16GB, 1TB config) run GLM-5.2?

No. GLM-5.2 needs ~473.1 GB even at Q4_K_M, but Samsung Galaxy S26 Ultra (16GB, 1TB config) only has ~12 GB usable.

How much memory does GLM-5.2 need?

Samsung Galaxy S26 Ultra (16GB, 1TB config) does not have enough memory. At Q4_K_M the weights are ~465.83 GB; with KV cache and runtime overhead, budget ~473.1 GB at a 4k context. It is a Mixture-of-Experts model (744B total / 40B active), so all experts must stay in memory; memory tracks total params, not active params.

What is the best tool to run GLM-5.2 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.