Deploy Gemma 2B on Android Smartphone

Overview

Run Gemma 2B directly on Android Smartphone for on-device mobile AI. Local inference offers privacy, zero latency, and no ongoing API costs.

Specs: Qualcomm NPU · 6-12GB

Installation

bash
Install Ollama — easiest local inference runtime
curl -fsSL https://ollama.com/install.sh | sh
Verify installation
ollama --version

Download Model

bash
Pull Gemma 2B (downloads GGUF quantized weights automatically)
ollama pull gemma-2b
Run interactive chat
ollama run gemma-2b
Start API server
ollama serve
API available at http://localhost:11434

Python Integration

python
import httpx
from typing import Iterator
class LocalAI:
    """Interface to local Gemma 2B running on Android Smartphone."""
    
    BASE_URL = "http://localhost:11434"
    MODEL = "gemma-2b"
    
    def chat(self, message: str, system: str = "") -> str:
        """Single-turn chat."""
        resp = httpx.post(
            f"{self.BASE_URL}/api/chat",
            json={
                "model": self.MODEL,
                "messages": [
                    {"role": "system", "content": system},
                    {"role": "user", "content": message}
                ],
                "stream": False
            },
            timeout=120
        )
        resp.raise_for_status()
        return resp.json()["message"]["content"]
    
    def stream(self, message: str) -> Iterator[str]:
        """Streaming chat for real-time output."""
        with httpx.stream(
            "POST",
            f"{self.BASE_URL}/api/chat",
            json={"model": self.MODEL, "messages": [{"role": "user", "content": message}], "stream": True},
            timeout=120
        ) as r:
            for line in r.iter_lines():
                if line:
                    import json
                    chunk = json.loads(line)
                    if not chunk.get("done"):
                        yield chunk["message"]["content"]
Usage
ai = LocalAI()
response = ai.chat("Help me with on-device mobile AI")
print(response)
Streaming
for token in ai.stream("Explain on-device mobile AI step by step"):
    print(token, end="", flush=True)

Custom Modelfile

bash
Create optimized configuration for on-device mobile AI
cat > Modelfile << 'MODELEOF'
FROM gemma-2b
PARAMETER num_ctx 4096
PARAMETER temperature 0.7
PARAMETER top_p 0.9
SYSTEM "You are an AI assistant specialized in on-device mobile AI. You run locally on Android Smartphone. Be concise, accurate, and helpful."
MODELEOF
ollama create on-device-mobile-AI-assistant -f Modelfile
ollama run on-device-mobile-AI-assistant

Performance Profile

MetricValue

HardwareQualcomm NPU Memory6-12GB Speed10-40 tokens/sec (CPU) / 40-100+ tok/s (GPU) First token<200ms (GPU) / <1s (CPU) Context4096-32768 tokens Cost$0 (after hardware)

Production Setup with FastAPI

python
from fastapi import FastAPI
from pydantic import BaseModel
app = FastAPI(title="Android Smartphone AI API")
ai = LocalAI()
class ChatRequest(BaseModel):
    message: str
    system: str = ""
class ChatResponse(BaseModel):
    response: str
    model: str
    device: str
@app.post("/chat", response_model=ChatResponse)
async def chat_endpoint(req: ChatRequest):
    response = ai.chat(req.message, req.system)
    return ChatResponse(response=response, model="Gemma 2B", device="Android Smartphone")
@app.get("/health")
async def health():
    return {"status": "ok", "model": "Gemma 2B", "device": "Android Smartphone"}

Troubleshooting

Slow inference: Switch to Q4_K_M quantization, reduce context window Out of memory: Use smaller model or Q3_K_S quant GPU not used: Install CUDA/Metal drivers, check ollama logs High latency: Warm up model by sending a dummy request on startup

Resources

Ollama library: https://ollama.com/library

GGUF format: https://github.com/ggerganov/llama.cpp

Hardware guide: https://ollama.com/blog/hardware-recommendations