text model · Llama · Android
Can I run Llama 3.2 1B on Google Pixel 9 Pro?
Yes. Llama 3.2 1B runs on Google Pixel 9 Pro at Q4_K_M (~1.8 GB of ~10.5 GB usable).
Runs at Q4_K_M using ~1.8 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 9 Pro leaves ~8.7 GB of headroom, room to step up to FP16 for higher quality.
- Q4_K_M needed
- ~1.8 GB
- Usable on device
- ~10.5 GB
- Device memory
- 16 GB
- Best quant
- Q4_K_M
Which quant fits
How to run it
On Android use PocketPal AI (Polished app, download GGUF and run offline.).
- Parameters
- 1B
- Q4_K_M size
- 0.81 GB
- Q8_0 size
- 1.32 GB
- Context
- 128k
- Ollama tag
- llama3.2:1b
- Memory
- 16 GB ram
- Usable for weights
- ~10.5 GB
- Best runtime
- llama.cpp (PocketPal) or MLC-LLM (Adreno GPU path)
You could also run
Run Llama 3.2 1B on other hardware
FAQ
Can Google Pixel 9 Pro run Llama 3.2 1B?
Yes. Llama 3.2 1B runs on Google Pixel 9 Pro at Q4_K_M (~1.8 GB of ~10.5 GB usable).
How much memory does Llama 3.2 1B need?
Google Pixel 9 Pro has room to spare. At Q4_K_M the weights are ~0.81 GB; with KV cache and runtime overhead, budget ~1.8 GB at a 4k context.
What is the best tool to run Llama 3.2 1B 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/llama-3.2-1b/pixel-9-pro) Sources
- 9to5google.com
- androidpolice.com
- comparigon.com
- github.com/a-ghorbani
- github.com/google-ai-edge
- github.com/mlc-ai
- github.com/shubham0204
- github.com/Vali-98
- gorilla.cs.berkeley.edu
- gsmarena.com/google_pixel_9_pro-12424.php
- gsmarena.com/google_pixel_9_pro-13218.php
- huggingface.co
- layla-network.ai
- lmarena.ai
- mlc.ai
- ollama.com/library/llama3.2
- ollama.com/library/llama3.2/tags
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.