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text model · SmolLM2 · Android

Can I run SmolLM2 135M on Google Pixel 10 Pro?

Compatibility verdict VRAM threshold engine
Yes, it runs usable speed ~325 tok/s est.

Yes. SmolLM2 135M runs on Google Pixel 10 Pro at Q4_K_M (~1 GB of ~10.5 GB usable).

Needs ~1 GB Device usable ~10.5 GB

Runs at Q4_K_M using ~1 GB of ~10.5 GB usable. You have room for FP16 for higher quality.

That figure is at a 4k context and moves about ±15% as context length changes. Google Pixel 10 Pro leaves ~9.5 GB of headroom, room to step up to FP16 for higher quality.

Q4_K_M needed
~1 GB
Usable on device
~10.5 GB
Device memory
16 GB
Best quant
Q4_K_M
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Which quant fits

Quant ladder vs ~10.5 GB usable
Q2_K
~1 GB
Q3_K_M
~1 GB
Q4_K_M
~1 GB
Q5_K_M
~1 GB
Q6_K
~1 GB
Q8_0
~1 GB
FP16
~1.2 GB
The line marks Google Pixel 10 Pro's ~10.5 GB budget; rungs past it are too large.

Running cost · estimate

Power & economics est.
Power draw
~7 W
Electricity / 1M tokens
~$0
Pays for itself after
~1,998M tok

At ~$0.15/kWh and the estimated ~325 tok/s, a million generated tokens costs about $0 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$999 Google Pixel 10 Pro pays for itself after roughly 1,998 million tokens, so local hardware is mostly a fixed cost, not a per-token one. TDP is the peak draw, so this is an upper bound. Assumptions.

How to run it

On Android use PocketPal AI (Polished app, download GGUF and run offline.).

Model SmolLM2
Parameters
0.135B
Q4_K_M size
0.105 GB
Q8_0 size
0.145 GB
Context
2k
Ollama tag
smollm2:135m
Full SmolLM2 135M requirements →
Device Android
Memory
16 GB ram
Usable for weights
~10.5 GB
Power draw
~7 W
Best runtime
llama.cpp (PocketPal) or MLC-LLM (Adreno GPU path)
Best models for Google Pixel 10 Pro →

You could also run

Run SmolLM2 135M on other hardware

FAQ

Can Google Pixel 10 Pro run SmolLM2 135M?

Yes. SmolLM2 135M runs on Google Pixel 10 Pro at Q4_K_M (~1 GB of ~10.5 GB usable).

How much memory does SmolLM2 135M need?

Google Pixel 10 Pro has room to spare. At Q4_K_M the weights are ~0.105 GB; with KV cache and runtime overhead, budget ~1 GB at a 4k context.

What is the best tool to run SmolLM2 135M 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.