text model · OpenELM · Android
Can I run Apple OpenELM 3B on Samsung Galaxy S26 Ultra (16GB, 1TB config)?
Yes. Apple OpenELM 3B runs on Samsung Galaxy S26 Ultra (16GB, 1TB config) at Q4_K_M (~3 GB of ~12 GB usable).
Runs at Q4_K_M using ~3 GB of ~12 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. Samsung Galaxy S26 Ultra (16GB, 1TB config) leaves ~9 GB of headroom, room to step up to FP16 for higher quality.
- Q4_K_M needed
- ~3 GB
- Usable on device
- ~12 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
- 3B
- Q4_K_M size
- 1.76 GB
- Q8_0 size
- 3.01 GB
- Context
- 2k
- Memory
- 16 GB ram
- Usable for weights
- ~12 GB
- Best runtime
- llama.cpp (PocketPal) or MLC-LLM (Adreno GPU path)
You could also run
Run Apple OpenELM 3B on other hardware
FAQ
Can Samsung Galaxy S26 Ultra (16GB, 1TB config) run Apple OpenELM 3B?
Yes. Apple OpenELM 3B runs on Samsung Galaxy S26 Ultra (16GB, 1TB config) at Q4_K_M (~3 GB of ~12 GB usable).
How much memory does Apple OpenELM 3B need?
Samsung Galaxy S26 Ultra (16GB, 1TB config) has room to spare. At Q4_K_M the weights are ~1.76 GB; with KV cache and runtime overhead, budget ~3 GB at a 4k context.
What is the best tool to run Apple OpenELM 3B 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.
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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.