text model · Yi · Android
Can I run Yi 1.5 34B on Google Pixel 10 Pro?
No. Yi 1.5 34B needs ~21.4 GB even at Q4_K_M, but Google Pixel 10 Pro only has ~10.5 GB usable.
Needs ~21.4 GB even at Q4_K_M, but only ~10.5 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 10.9 GB: Yi 1.5 34B needs roughly 21.4 GB at Q4_K_M and Google Pixel 10 Pro leaves only about 10.5 GB usable for a model. The lightest tracked hardware that runs Yi 1.5 34B is the Nvidia GeForce RTX 4090 (24GB) at 24 GB. See Yi 1.5 34B on Nvidia GeForce RTX 4090 (24GB).
- Q4_K_M needed
- ~21.4 GB
- Usable on device
- ~10.5 GB
- Device memory
- 16 GB
Which quant fits
How to run it
On Android use PocketPal AI (Polished app, download GGUF and run offline.).
- Parameters
- 34B
- Q4_K_M size
- 19.24 GB
- Q8_0 size
- 34.03 GB
- Context
- 32k
- 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)
What you can run instead
Run Yi 1.5 34B on other hardware
FAQ
Can Google Pixel 10 Pro run Yi 1.5 34B?
No. Yi 1.5 34B needs ~21.4 GB even at Q4_K_M, but Google Pixel 10 Pro only has ~10.5 GB usable.
How much memory does Yi 1.5 34B need?
Google Pixel 10 Pro does not have enough memory. At Q4_K_M the weights are ~19.24 GB; with KV cache and runtime overhead, budget ~21.4 GB at a 4k context.
What is the best tool to run Yi 1.5 34B 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/yi-1.5-34b/pixel-10-pro) 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.