text model · Ornith · Android

Can I run Ornith 1.0 35B on Samsung Galaxy S24 Ultra?

Compatibility verdict VRAM threshold engine

No, not enough memory would not load

No. Ornith 1.0 35B needs ~23.2 GB even at Q4_K_M, but Samsung Galaxy S24 Ultra only has ~8.5 GB usable.

Needs ~23.2 GB Device usable ~8.5 GB

Needs ~23.2 GB even at Q4_K_M, but only ~8.5 GB is usable.

That figure is at a 4k context and moves about ±15% as context length changes.

The gap is about 14.7 GB: Ornith 1.0 35B needs roughly 23.2 GB at Q4_K_M and Samsung Galaxy S24 Ultra leaves only about 8.5 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: ~8.5 GB
Device memory: 12 GB

Share on X Share on Reddit

Which quant fits

Quant ladder vs ~8.5 GB usable

Q2_K

~16.9 GB

Q3_K_M

~19.3 GB

Q4_K_M

~23.2 GB

Q5_K_M

~27.1 GB

Q6_K

~30.9 GB

Q8_0

~39.2 GB

FP16

~71.2 GB

The line marks Samsung Galaxy S24 Ultra's ~8.5 GB budget; rungs past it are too large.

How to run it

On Android use PocketPal AI (Polished app, download GGUF and run offline.).

Model Ornith

Parameters: 35B (MoE, 3B active)
Q4_K_M size: 21 GB
Q8_0 size: 37 GB
Context: 256k
Ollama tag: ornith:35b

Full Ornith 1.0 35B requirements →

Device Android

Memory: 12 GB ram
Usable for weights: ~8.5 GB
Power draw: ~8 W
Best runtime: llama.cpp (PocketPal) or MLC-LLM (Adreno GPU path)

Best models for Samsung Galaxy S24 Ultra →

What you can run instead

Run Ornith 1.0 35B on other hardware

Nvidia GeForce RTX 5090 (32GB)32GB RAM Laptop (CPU/iGPU only)Apple M4 Pro (48GB)Apple M5 Pro (48GB)Apple M4 Max (64GB)Apple M4 Max (128GB)

FAQ

Can Samsung Galaxy S24 Ultra run Ornith 1.0 35B?

No. Ornith 1.0 35B needs ~23.2 GB even at Q4_K_M, but Samsung Galaxy S24 Ultra only has ~8.5 GB usable.

How much memory does Ornith 1.0 35B need?

Samsung Galaxy S24 Ultra 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.

Markdown

[![Ornith 1.0 35B on Samsung Galaxy S24 Ultra](https://localmodel.run/badge/ornith-1.0-35b/samsung-s24-ultra.svg)](https://localmodel.run/can-i-run/ornith-1.0-35b/samsung-s24-ultra)

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.