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text model · Mistral · Android

Can I run Mixtral 8x7B on Generic Android Phone (12GB RAM)?

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

No. Mixtral 8x7B needs ~28.9 GB even at Q4_K_M, but Generic Android Phone (12GB RAM) only has ~8.5 GB usable.

Needs ~28.9 GB Device usable ~8.5 GB

Needs ~28.9 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 20.4 GB: Mixtral 8x7B needs roughly 28.9 GB at Q4_K_M and Generic Android Phone (12GB RAM) leaves only about 8.5 GB usable for a model. The lightest tracked hardware that runs Mixtral 8x7B is the Nvidia GeForce RTX 5090 (32GB) at 32 GB. See Mixtral 8x7B on Nvidia GeForce RTX 5090 (32GB).

Q4_K_M needed
~28.9 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
~22 GB
Q3_K_M
~25.2 GB
Q4_K_M
~28.9 GB
Q5_K_M
~35.7 GB
Q6_K
~40.7 GB
Q8_0
~48.6 GB
FP16
~95.8 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 Mistral
Parameters
46.7B (MoE, 12.9B active)
Q4_K_M size
26.49 GB
Q8_0 size
46.22 GB
Context
32k
Ollama tag
mixtral:8x7b
Full Mixtral 8x7B 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

Run Mixtral 8x7B on other hardware

FAQ

Can Generic Android Phone (12GB RAM) run Mixtral 8x7B?

No. Mixtral 8x7B needs ~28.9 GB even at Q4_K_M, but Generic Android Phone (12GB RAM) only has ~8.5 GB usable.

How much memory does Mixtral 8x7B need?

Generic Android Phone (12GB RAM) does not have enough memory. At Q4_K_M the weights are ~26.49 GB; with KV cache and runtime overhead, budget ~28.9 GB at a 4k context. It is a Mixture-of-Experts model (46.7B total / 12.9B active), so all experts must stay in memory; memory tracks total params, not active params.

What is the best tool to run Mixtral 8x7B 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.