text model · Ornith · Android
Can I run Ornith 1.0 9B on Google Pixel 10 Pro?
Yes. Ornith 1.0 9B runs on Google Pixel 10 Pro at Q4_K_M (~7.1 GB of ~10.5 GB usable).
Runs at Q4_K_M using ~7.1 GB of ~10.5 GB usable.
That figure is at a 4k context and moves about ±15% as context length changes. Google Pixel 10 Pro leaves ~3.4 GB of headroom.
- Q4_K_M needed
- ~7.1 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
- ~$0.05
- Pays for itself after
- ~2,220M tok
At ~$0.15/kWh and the estimated ~6 tok/s, a million generated tokens costs about $0.05 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$999 Google Pixel 10 Pro pays for itself after roughly 2,220 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
- 9B
- Q4_K_M size
- 5.6 GB
- Q8_0 size
- 9.5 GB
- Context
- 256k
- Ollama tag
- ornith:9b
- 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 Ornith 1.0 9B on other hardware
FAQ
Can Google Pixel 10 Pro run Ornith 1.0 9B?
Yes. Ornith 1.0 9B runs on Google Pixel 10 Pro at Q4_K_M (~7.1 GB of ~10.5 GB usable).
How much memory does Ornith 1.0 9B need?
Google Pixel 10 Pro has room to spare. At Q4_K_M the weights are ~5.6 GB; with KV cache and runtime overhead, budget ~7.1 GB at a 4k context.
What is the best tool to run Ornith 1.0 9B 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/ornith-1.0-9b/pixel-10-pro) Sources
- 9to5google.com
- deep-reinforce.com
- 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/deepreinforce-ai/Ornith-1.0-9B
- huggingface.co/deepreinforce-ai/Ornith-1.0-9B-GGUF
- layla-network.ai
- ollama.com
- 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.