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text model · MiniMax · Android

Can I run MiniMax M3 on Generic Android Phone (8GB RAM)?

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

No. MiniMax M3 needs ~269.8 GB even at Q4_K_M, but Generic Android Phone (8GB RAM) only has ~4.5 GB usable.

Needs ~269.8 GB Device usable ~4.5 GB

Needs ~269.8 GB even at Q4_K_M, but only ~4.5 GB is usable.

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

The gap is about 265.3 GB: MiniMax M3 needs roughly 269.8 GB at Q4_K_M and Generic Android Phone (8GB RAM) leaves only about 4.5 GB usable for a model. No single tracked device has enough memory; MiniMax M3 needs a multi-GPU or high-memory rig.

Q4_K_M needed
~269.8 GB
Usable on device
~4.5 GB
Device memory
8 GB
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Which quant fits

Quant ladder vs ~4.5 GB usable
Q2_K
~185 GB
Q3_K_M
~215 GB
Q4_K_M
~269.8 GB
Q5_K_M
~310.8 GB
Q6_K
~356.8 GB
Q8_0
~458.5 GB
FP16
~857.8 GB
The line marks Generic Android Phone (8GB RAM)'s ~4.5 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 MiniMax
Parameters
428B (MoE, 23B active)
Q4_K_M size
264.03 GB
Q8_0 size
452.71 GB
Context
1000k
Full MiniMax M3 requirements →
Device Android
Memory
8 GB ram
Usable for weights
~4.5 GB
Best runtime
llama.cpp (PocketPal or SmolChat)
Best models for Generic Android Phone (8GB RAM) →

What you can run instead

FAQ

Can Generic Android Phone (8GB RAM) run MiniMax M3?

No. MiniMax M3 needs ~269.8 GB even at Q4_K_M, but Generic Android Phone (8GB RAM) only has ~4.5 GB usable.

How much memory does MiniMax M3 need?

Generic Android Phone (8GB RAM) does not have enough memory. At Q4_K_M the weights are ~264.03 GB; with KV cache and runtime overhead, budget ~269.8 GB at a 4k context. It is a Mixture-of-Experts model (428B total / 23B active), so all experts must stay in memory; memory tracks total params, not active params.

What is the best tool to run MiniMax M3 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.