text model · Mistral · Android
Can I run Mixtral 8x7B on Google Pixel 9 Pro?
No. Mixtral 8x7B needs ~28.9 GB even at Q4_K_M, but Google Pixel 9 Pro only has ~10.5 GB usable.
Needs ~28.9 GB even at Q4_K_M, but only ~10.5 GB is usable.
That figure is at a 4k context and moves about ±15% as context length changes.
The gap is about 18.4 GB: Mixtral 8x7B needs roughly 28.9 GB at Q4_K_M and Google Pixel 9 Pro leaves only about 10.5 GB usable for a model. The lightest tracked hardware that runs Mixtral 8x7B is the Nvidia GeForce RTX 5090 (32GB) at 32 GB. See Mixtral 8x7B on Nvidia GeForce RTX 5090 (32GB).
- Q4_K_M needed
- ~28.9 GB
- Usable on device
- ~10.5 GB
- Device memory
- 16 GB
Which quant fits
How to run it
On Android use PocketPal AI (Polished app, download GGUF and run offline.).
- Parameters
- 46.7B (MoE, 12.9B active)
- Q4_K_M size
- 26.49 GB
- Q8_0 size
- 46.22 GB
- Context
- 32k
- Ollama tag
- mixtral:8x7b
- Memory
- 16 GB ram
- Usable for weights
- ~10.5 GB
- Best runtime
- llama.cpp (PocketPal) or MLC-LLM (Adreno GPU path)
What you can run instead
Run Mixtral 8x7B on other hardware
FAQ
Can Google Pixel 9 Pro run Mixtral 8x7B?
No. Mixtral 8x7B needs ~28.9 GB even at Q4_K_M, but Google Pixel 9 Pro only has ~10.5 GB usable.
How much memory does Mixtral 8x7B need?
Google Pixel 9 Pro does not have enough memory. At Q4_K_M the weights are ~26.49 GB; with KV cache and runtime overhead, budget ~28.9 GB at a 4k context. It is a Mixture-of-Experts model (46.7B total / 12.9B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run Mixtral 8x7B 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/mixtral-8x7b/pixel-9-pro) Sources
- 9to5google.com
- androidpolice.com
- comparigon.com
- github.com/a-ghorbani
- github.com/google-ai-edge
- github.com/mlc-ai
- github.com/shubham0204
- github.com/Vali-98
- gsmarena.com/google_pixel_9_pro-12424.php
- gsmarena.com/google_pixel_9_pro-13218.php
- huggingface.co/MaziyarPanahi
- huggingface.co/mistralai
- layla-network.ai
- mlc.ai
- ollama.com/library/mixtral
- ollama.com/library/mixtral/tags
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.