text model · Llama 4 · Android
Can I run Llama 4 Maverick on Google Pixel 9 Pro?
No. Llama 4 Maverick needs ~231.7 GB even at Q4_K_M, but Google Pixel 9 Pro only has ~10.5 GB usable.
Needs ~231.7 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 221.2 GB: Llama 4 Maverick needs roughly 231.7 GB at Q4_K_M and Google Pixel 9 Pro leaves only about 10.5 GB usable for a model. No single tracked device has enough memory; Llama 4 Maverick needs a multi-GPU or high-memory rig.
- Q4_K_M needed
- ~231.7 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
- 400B (MoE, 17B active)
- Q4_K_M size
- 226.09 GB
- Q8_0 size
- 396.57 GB
- Context
- 1000k
- Ollama tag
- llama4:128x17b
- 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
FAQ
Can Google Pixel 9 Pro run Llama 4 Maverick?
No. Llama 4 Maverick needs ~231.7 GB even at Q4_K_M, but Google Pixel 9 Pro only has ~10.5 GB usable.
How much memory does Llama 4 Maverick need?
Google Pixel 9 Pro does not have enough memory. At Q4_K_M the weights are ~226.09 GB; with KV cache and runtime overhead, budget ~231.7 GB at a 4k context. It is a Mixture-of-Experts model (400B total / 17B active), so all experts must stay in memory; memory tracks total params, not active params.
What is the best tool to run Llama 4 Maverick 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/llama-4-maverick/pixel-9-pro) Sources
- 9to5google.com
- aider.chat
- androidpolice.com
- comparigon.com
- github.com/a-ghorbani
- github.com/google-ai-edge
- github.com/mlc-ai
- github.com/shubham0204
- github.com/Vali-98
- gorilla.cs.berkeley.edu
- gsmarena.com/google_pixel_9_pro-12424.php
- gsmarena.com/google_pixel_9_pro-13218.php
- huggingface.co/meta-llama
- huggingface.co/unsloth
- layla-network.ai
- mlc.ai
- ollama.com
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.