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text model · DeepSeek-V4 · Android

Can I run DeepSeek-V4-Pro on Generic Android Phone (12GB RAM)?

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

No. DeepSeek-V4-Pro needs ~965 GB even at Q4_K_M, but Generic Android Phone (12GB RAM) only has ~8.5 GB usable.

Needs ~965 GB Device usable ~8.5 GB

Needs ~965 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 956.5 GB: DeepSeek-V4-Pro needs roughly 965 GB at Q4_K_M and Generic Android Phone (12GB RAM) leaves only about 8.5 GB usable for a model. No single tracked device has enough memory; DeepSeek-V4-Pro needs a multi-GPU or high-memory rig.

Q4_K_M needed
~965 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
~680.4 GB
Q3_K_M
~792.4 GB
Q4_K_M
~965 GB
Q5_K_M
~1150.4 GB
Q6_K
~1322.4 GB
Q8_0
~1682.2 GB
FP16
~3210.4 GB
The line marks Generic Android Phone (12GB RAM)'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 DeepSeek-V4
Parameters
1600B (MoE, 49B active)
Q4_K_M size
954.58 GB
Q8_0 size
1671.82 GB
Context
1000k
Full DeepSeek-V4-Pro requirements →
Device Android
Memory
12 GB ram
Usable for weights
~8.5 GB
Best runtime
llama.cpp (PocketPal) or MLC-LLM
Best models for Generic Android Phone (12GB RAM) →

What you can run instead

FAQ

Can Generic Android Phone (12GB RAM) run DeepSeek-V4-Pro?

No. DeepSeek-V4-Pro needs ~965 GB even at Q4_K_M, but Generic Android Phone (12GB RAM) only has ~8.5 GB usable.

How much memory does DeepSeek-V4-Pro need?

Generic Android Phone (12GB RAM) does not have enough memory. At Q4_K_M the weights are ~954.58 GB; with KV cache and runtime overhead, budget ~965 GB at a 4k context. It is a Mixture-of-Experts model (1600B total / 49B active), so all experts must stay in memory; memory tracks total params, not active params.

What is the best tool to run DeepSeek-V4-Pro 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.