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text model · dots.llm · Android

Can I run dots.llm1 on Generic Android Phone (12GB RAM)?

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

No. dots.llm1 needs ~98.1 GB even at Q4_K_M, but Generic Android Phone (12GB RAM) only has ~8.5 GB usable.

Needs ~98.1 GB Device usable ~8.5 GB

Needs ~98.1 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 89.6 GB: dots.llm1 needs roughly 98.1 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 dots.llm1 is the Apple M3 Ultra (256GB) at 256 GB. See dots.llm1 on Apple M3 Ultra (256GB).

Q4_K_M needed
~98.1 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
~63.2 GB
Q3_K_M
~73.1 GB
Q4_K_M
~98.1 GB
Q5_K_M
~104.9 GB
Q6_K
~120.1 GB
Q8_0
~155.6 GB
FP16
~289.7 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 dots.llm
Parameters
142B (MoE, 14B active)
Q4_K_M size
94.4 GB
Q8_0 size
151.9 GB
Context
32k
Full dots.llm1 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 dots.llm1 on other hardware

FAQ

Can Generic Android Phone (12GB RAM) run dots.llm1?

No. dots.llm1 needs ~98.1 GB even at Q4_K_M, but Generic Android Phone (12GB RAM) only has ~8.5 GB usable.

How much memory does dots.llm1 need?

Generic Android Phone (12GB RAM) does not have enough memory. At Q4_K_M the weights are ~94.4 GB; with KV cache and runtime overhead, budget ~98.1 GB at a 4k context. It is a Mixture-of-Experts model (142B total / 14B active), so all experts must stay in memory; memory tracks total params, not active params.

What is the best tool to run dots.llm1 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.