Can OneJev 0.8B run on Huawei Mate X7?

Estimated weights fit · custom workflow required

Weight fit is not phone app support

The tracked Q4_K_M weights are 0.7GB; the generic working-memory estimate is 1.4GB against 16GB usable. Image inputs need additional projector memory.

OneJev scores typed decisions in a single forward pass, not chat replies. The decode estimates below are not measured decision latency. The publisher's qev server and System One client are required; AICanRun has not verified a phone app route.

Not measured
decision latency
1.4GB
needed at Q4_K_M
Q4_K_M
quant checked
needs 1.4 GBusable 16 GB
Kirin 9030 Pro20 GB RAMCPU / GPUAlso sold with 12/16 GB

Where the memory goes

ComponentDetailGB
Model weightsQ4_K_M GGUF (0.7 GB) + mmap overhead0.7
KV cache4K context window0.1
Runtimellama.cpp + app overhead0.6
Total neededat Q4_K_M, 4K context1.4
Working budget20 GB RAM − Android system reserve16
Headroomremaining inside the working budget14.6

Pick your quant

QuantDownloadVerdictSpeed
Q2_K 0.5 GB✓ Runs great~69.1 tokens/s
IQ4_XS 0.6 GB✓ Runs great~57.6 tokens/s
Q3_K_M 0.6 GB✓ Runs great~57.6 tokens/s
Q4_K_S 0.6 GB✓ Runs great~57.6 tokens/s
Q4_K_M BEST HERE0.7 GB✓ Runs great~49.4 tokens/s
Q5_K_M 0.8 GB✓ Runs great~43.2 tokens/s
Q6_K 0.8 GB✓ Runs great~43.2 tokens/s
Q8_0 1.1 GB✓ Runs great~31.4 tokens/s

Use the right workflow for this model

The model fits in memory, but it is not an ordinary chat model.

This model is designed to return calibrated probabilities for typed choices from text or images. PocketPal does not provide the required the publisher's qev server and System One client; image inputs also need a vision projector, so the tracked file is not a beginner phone-chat path.

Read the publisher workflow ↗

If you only want a normal private chat, use this simpler model instead:

Check Llama 3.2 3B

Related checks

More on Huawei Mate X7
Qwen3 0.6BQwen3 1.7BLlama 3.2 1BLlama 3.2 3BGemma 3 1B
OneJev 0.8B on other phones
Galaxy S25 UltraGalaxy S25Galaxy S24 UltraGalaxy S24Galaxy S23 Ultra