text model · Kimi · Android

Can I run Kimi K2.7 Code on Google Pixel 10 Pro?

Compatibility verdict VRAM threshold engine

No, not enough memory would not load

No. Kimi K2.7 Code needs ~592.1 GB even at Q4_K_M, but Google Pixel 10 Pro only has ~10.5 GB usable.

Needs ~592.1 GB Device usable ~10.5 GB

Needs ~592.1 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 581.6 GB: Kimi K2.7 Code needs roughly 592.1 GB at Q4_K_M and Google Pixel 10 Pro leaves only about 10.5 GB usable for a model. No single tracked device has enough memory; Kimi K2.7 Code needs a multi-GPU or high-memory rig.

Q4_K_M needed: ~592.1 GB
Usable on device: ~10.5 GB
Device memory: 16 GB

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Which quant fits

Quant ladder vs ~10.5 GB usable

Q2_K

~427.2 GB

Q3_K_M

~497.2 GB

Q4_K_M

~592.1 GB

Q5_K_M

~720.9 GB

Q6_K

~828.4 GB

Q8_0

~1070.9 GB

FP16

~2008.4 GB

The line marks Google Pixel 10 Pro's ~10.5 GB budget; rungs past it are too large.

How to run it

On Android use PocketPal AI (Polished app, download GGUF and run offline.).

Model Kimi

Parameters: 1000B (MoE, 32B active)
Q4_K_M size: 583.71 GB
Context: 256k

Full Kimi K2.7 Code requirements →

Device Android

Memory: 16 GB ram
Usable for weights: ~10.5 GB
Power draw: ~7 W
Best runtime: llama.cpp (PocketPal) or MLC-LLM (Adreno GPU path)

Best models for Google Pixel 10 Pro →

What you can run instead

FAQ

Can Google Pixel 10 Pro run Kimi K2.7 Code?

No. Kimi K2.7 Code needs ~592.1 GB even at Q4_K_M, but Google Pixel 10 Pro only has ~10.5 GB usable.

How much memory does Kimi K2.7 Code need?

Google Pixel 10 Pro does not have enough memory. At Q4_K_M the weights are ~583.71 GB; with KV cache and runtime overhead, budget ~592.1 GB at a 4k context. It is a Mixture-of-Experts model (1000B total / 32B active), so all experts must stay in memory; memory tracks total params, not active params.

What is the best tool to run Kimi K2.7 Code 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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Markdown

[![Kimi K2.7 Code on Google Pixel 10 Pro](https://localmodel.run/badge/kimi-k2.7-code/pixel-10-pro.svg)](https://localmodel.run/can-i-run/kimi-k2.7-code/pixel-10-pro)

Sources

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.