text model · Olmo · Android
Can I run Olmo 3.1 32B Instruct on Generic Android Phone (12GB RAM)?
No. Olmo 3.1 32B Instruct needs ~20.3 GB even at Q4_K_M, but Generic Android Phone (12GB RAM) only has ~8.5 GB usable.
Needs ~20.3 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 11.8 GB: Olmo 3.1 32B Instruct needs roughly 20.3 GB at Q4_K_M and Generic Android Phone (12GB RAM) leaves only about 8.5 GB usable for a model. The lightest tracked hardware that runs Olmo 3.1 32B Instruct is the Nvidia GeForce RTX 4090 (24GB) at 24 GB. See Olmo 3.1 32B Instruct on Nvidia GeForce RTX 4090 (24GB).
- Q4_K_M needed
- ~20.3 GB
- Usable on device
- ~8.5 GB
- Device memory
- 12 GB
Which quant fits
How to run it
On Android use PocketPal AI (Polished app, download GGUF and run offline.).
- Parameters
- 32B
- Q4_K_M size
- 18.14 GB
- Q8_0 size
- 31.9 GB
- Context
- 64k
- Ollama tag
- olmo-3.1:32b-instruct
- Memory
- 12 GB ram
- Usable for weights
- ~8.5 GB
- Best runtime
- llama.cpp (PocketPal) or MLC-LLM
What you can run instead
Run Olmo 3.1 32B Instruct on other hardware
FAQ
Can Generic Android Phone (12GB RAM) run Olmo 3.1 32B Instruct?
No. Olmo 3.1 32B Instruct needs ~20.3 GB even at Q4_K_M, but Generic Android Phone (12GB RAM) only has ~8.5 GB usable.
How much memory does Olmo 3.1 32B Instruct need?
Generic Android Phone (12GB RAM) does not have enough memory. At Q4_K_M the weights are ~18.14 GB; with KV cache and runtime overhead, budget ~20.3 GB at a 4k context.
What is the best tool to run Olmo 3.1 32B Instruct 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/olmo-3.1-32b-instruct/android-generic-12gb) Sources
- github.com/a-ghorbani
- github.com/google-ai-edge
- github.com/mlc-ai
- github.com/shubham0204
- github.com/Vali-98
- huggingface.co/allenai
- huggingface.co/bartowski/allenai_Olmo-3.1-32B-Instruct-GGUF
- huggingface.co/bartowski/Meta-Llama-3.1-8B-Instruct-GGUF
- layla-network.ai
- ollama.com/library/llama3.1:8b
- ollama.com/library/olmo-3.1
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.