text model · Mistral · Android
Can I run Mistral Small 3.1 24B on Generic Android Phone (8GB RAM)?
No. Mistral Small 3.1 24B needs ~15.4 GB even at Q4_K_M, but Generic Android Phone (8GB RAM) only has ~4.5 GB usable.
Needs ~15.4 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 10.9 GB: Mistral Small 3.1 24B needs roughly 15.4 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 Mistral Small 3.1 24B is the Apple M4 (24GB) at 24 GB. See Mistral Small 3.1 24B on Apple M4 (24GB).
- Q4_K_M needed
- ~15.4 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
- 24B
- Q4_K_M size
- 13.35 GB
- Q8_0 size
- 23.33 GB
- Context
- 128k
- Ollama tag
- mistral-small3.1:24b
- Memory
- 8 GB ram
- Usable for weights
- ~4.5 GB
- Best runtime
- llama.cpp (PocketPal or SmolChat)
What you can run instead
Run Mistral Small 3.1 24B on other hardware
FAQ
Can Generic Android Phone (8GB RAM) run Mistral Small 3.1 24B?
No. Mistral Small 3.1 24B needs ~15.4 GB even at Q4_K_M, but Generic Android Phone (8GB RAM) only has ~4.5 GB usable.
How much memory does Mistral Small 3.1 24B need?
Generic Android Phone (8GB RAM) does not have enough memory. At Q4_K_M the weights are ~13.35 GB; with KV cache and runtime overhead, budget ~15.4 GB at a 4k context.
What is the best tool to run Mistral Small 3.1 24B 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/mistral-small-3.1-24b/android-generic-8gb) Sources
- github.com/a-ghorbani
- github.com/google-ai-edge
- github.com/mlc-ai
- github.com/shubham0204
- github.com/Vali-98
- huggingface.co/bartowski/Llama-3.2-1B-Instruct-GGUF
- huggingface.co/bartowski/mistralai_Mistral-Small-3.1-24B-Instruct-2503-GGUF
- huggingface.co/mistralai
- layla-network.ai
- ollama.com/library/llama3.2:1b
- ollama.com/library/mistral-small3.1
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.