text model · Ornith · Android
Can I run Ornith 1.0 35B on Generic Android Phone (8GB RAM)?
No. Ornith 1.0 35B needs ~23.2 GB even at Q4_K_M, but Generic Android Phone (8GB RAM) only has ~4.5 GB usable.
Needs ~23.2 GB even at Q4_K_M, but only ~4.5 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 18.7 GB: Ornith 1.0 35B needs roughly 23.2 GB at Q4_K_M and Generic Android Phone (8GB RAM) leaves only about 4.5 GB usable for a model. The lightest tracked hardware that runs Ornith 1.0 35B is the Nvidia GeForce RTX 5090 (32GB) at 32 GB. See Ornith 1.0 35B on Nvidia GeForce RTX 5090 (32GB).
- Q4_K_M needed
- ~23.2 GB
- Usable on device
- ~4.5 GB
- Device memory
- 8 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
- 21 GB
- Q8_0 size
- 37 GB
- Context
- 256k
- Ollama tag
- ornith:35b
- Memory
- 8 GB ram
- Usable for weights
- ~4.5 GB
- Best runtime
- llama.cpp (PocketPal or SmolChat)
What you can run instead
Run Ornith 1.0 35B on other hardware
FAQ
Can Generic Android Phone (8GB RAM) run Ornith 1.0 35B?
No. Ornith 1.0 35B needs ~23.2 GB even at Q4_K_M, but Generic Android Phone (8GB RAM) only has ~4.5 GB usable.
How much memory does Ornith 1.0 35B need?
Generic Android Phone (8GB RAM) does not have enough memory. At Q4_K_M the weights are ~21 GB; with KV cache and runtime overhead, budget ~23.2 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 1.0 35B 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-1.0-35b/android-generic-8gb) Sources
- deep-reinforce.com
- github.com/a-ghorbani
- github.com/google-ai-edge
- github.com/mlc-ai
- github.com/shubham0204
- github.com/Vali-98
- huggingface.co/bartowski
- huggingface.co/deepreinforce-ai/Ornith-1.0-35B
- huggingface.co/deepreinforce-ai/Ornith-1.0-35B-GGUF
- layla-network.ai
- ollama.com/library/llama3.2:1b
- ollama.com/library/ornith
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.