text model · Ornith · Android
Can I run Ornith 1.5 35B-A3B on Google Pixel 10 Pro?
No. Ornith 1.5 35B-A3B needs ~22.4 GB even at Q4_K_M, but Google Pixel 10 Pro only has ~10.5 GB usable.
Needs ~22.4 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 11.9 GB: Ornith 1.5 35B-A3B needs roughly 22.4 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 Ornith 1.5 35B-A3B is the Nvidia GeForce RTX 4090 (24GB) at 24 GB. See Ornith 1.5 35B-A3B on Nvidia GeForce RTX 4090 (24GB).
- Q4_K_M needed
- ~22.4 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
- 35B (MoE, 3B active)
- Q4_K_M size
- 20.22 GB
- Q8_0 size
- 35.21 GB
- Context
- 256k
- Ollama tag
- ornith-1.5:35b
- 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 Ornith 1.5 35B-A3B on other hardware
FAQ
Can Google Pixel 10 Pro run Ornith 1.5 35B-A3B?
No. Ornith 1.5 35B-A3B needs ~22.4 GB even at Q4_K_M, but Google Pixel 10 Pro only has ~10.5 GB usable.
How much memory does Ornith 1.5 35B-A3B need?
Google Pixel 10 Pro does not have enough memory. At Q4_K_M the weights are ~20.22 GB; with KV cache and runtime overhead, budget ~22.4 GB at a 4k context. It is a Mixture-of-Experts model (35B total / 3B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run Ornith 1.5 35B-A3B 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-1.5-35b-a3b/pixel-10-pro) Sources
- 9to5google.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/ornith-ai/Ornith-1.5-35B-A3B
- huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-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.