text model · LFM · Android
Can I run LFM2 350M on Google Pixel 10 Pro?
Yes. LFM2 350M runs on Google Pixel 10 Pro at Q4_K_M (~1.4 GB of ~10.5 GB usable).
Runs at Q4_K_M using ~1.4 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 ~9.1 GB of headroom, room to step up to FP16 for higher quality.
- Q4_K_M needed
- ~1.4 GB
- Usable on device
- ~10.5 GB
- Device memory
- 16 GB
- Best quant
- Q4_K_M
Which quant fits
Running cost · estimate
- Power draw
- ~7 W
- Electricity / 1M tokens
- ~$0
- Pays for itself after
- ~1,998M tok
At ~$0.15/kWh and the estimated ~76 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.).
- Parameters
- 0.354B
- Q4_K_M size
- 0.45 GB
- Q8_0 size
- 0.35 GB
- Context
- 128k
- 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)
You could also run
Run LFM2 350M on other hardware
FAQ
Can Google Pixel 10 Pro run LFM2 350M?
Yes. LFM2 350M runs on Google Pixel 10 Pro at Q4_K_M (~1.4 GB of ~10.5 GB usable).
How much memory does LFM2 350M need?
Google Pixel 10 Pro has room to spare. At Q4_K_M the weights are ~0.45 GB; with KV cache and runtime overhead, budget ~1.4 GB at a 4k context.
What is the best tool to run LFM2 350M 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.
Embed this
A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/lfm2-350m/pixel-10-pro) Sources
- 9to5google.com
- arxiv.org
- gadgetversus.com
- github.com/a-ghorbani
- github.com/google-ai-edge
- github.com/mlc-ai
- github.com/shubham0204
- github.com/Vali-98
- gsmarena.com
- huggingface.co/LiquidAI/LFM2-350M
- huggingface.co/LiquidAI/LFM2-350M-GGUF
- layla-network.ai
- liquid.ai
- store.google.com/product/pixel_10_pro
- store.google.com/product/pixel_10_pro_specs
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.