pjt222/deploy-edge-ai-model

Deploy Edge AI Model

See Extended Examples for complete configuration files, quantization scripts, and benchmark templates.

Deploy ML models to edge devices with optimized inference, hardware acceleration, and on-device model management.

When to Use

• Deploying LLMs (Gemma 4, Phi, Llama) to mobile devices via Google AI Edge Gallery
• Converting models to TensorFlow Lite or ONNX for on-device inference
• Quantizing models to INT8/INT4 for reduced memory and faster inference
• Building Android/iOS apps with local AI capabilities
• Selecting hardware delegates (GPU, NPU, DSP, Hexagon, CoreML)
• Benchmarking inference latency and memory on target devices
• Deploying MediaPipe tasks (vision, text, audio) to mobile or embedded platforms

Inputs

• Required: Trained model (SavedModel, PyTorch, ONNX, or Hugging Face checkpoint)
• Required: Target platform (Android, iOS, Linux embedded, browser)
• Required: Target device constraints (RAM, storage, compute capability)
• Optional: Calibration dataset for post-training quantization
• Optional: Google AI Edge Gallery configuration for LLM deployment
• Optional: Hardware delegate preferences (GPU, NPU, CPU-only)

Procedure

Step 1: Evaluate Model for Edge Deployment

Assess model size, latency requirements, and target device capabilities.

# assess_model.py
import os
import tensorflow as tf

def assess_model_for_edge(saved_model_path, target_ram_mb=4096):
    """Evaluate whether a model is suitable for edge deployment."""
    model = tf.saved_model.load(saved_model_path)

    # Check model size on disk
    model_size_mb = sum(
        os.path.getsize(os.path.join(dp, f))
        for dp, _, filenames in os.walk(saved_model_path)
        for f in filenames
    ) / (1024 * 1024)

    print(f"Model size: {model_size_mb:.1f} MB")
    print(f"Target RAM: {target_ram_mb} MB")
    print(f"Size/RAM ratio: {model_size_mb / target_ram_mb:.2%}")

    if model_size_mb > target_ram_mb * 0.25:
        print("WARNING: Model exceeds 25% of device RAM - quantization recommended")
        return False
    return True

Edge deployment decision matrix:

| Model Size | Device RAM | Recommended Action | |-----------|-----------|-------------------| | < 50 MB | 2+ GB | Direct TFLite conversion | | 50-500 MB | 4+ GB | INT8 quantization + TFLite | | 500 MB-2 GB | 6+ GB | INT4 quantization + AI Edge Gallery | | 2-4 GB | 8+ GB | Gemma 4 via AI Edge Gallery with INT4 | | > 4 GB | 12+ GB | Weight streaming or cloud-edge hybrid |

Expected: Model assessment completes, size and RAM ratios calculated, quantization recommendation generated based on device constraints.

On failure: Verify SavedModel path is valid (ls saved_model/), check TensorFlow installation (python -c "import tensorflow"), ensure sufficient disk space for model loading, verify model format is supported.

Step 2: Deploy LLMs via Google AI Edge Gallery

Use Google AI Edge Gallery to deploy Gemma 4 and other LLMs to Android devices.

# Clone AI Edge Gallery
git clone https://github.com/nickoala/ai-edge-gallery.git
cd ai-edge-gallery

# Build the Android app
./gradlew assembleDebug

# Install on connected device
adb install -r app/build/outputs/apk/debug/app-debug.apk

Configure Gemma 4 model for AI Edge Gallery:

{
  "models": [
    {
      "name": "Gemma 4 2B IT",
      "url": "https://huggingface.co/google/gemma-4-2b-it-gpu-int4",
      "format": "tflite",
      "backend": "gpu",
      "config": {
        "max_tokens": 1024,
        "temperature": 0.7,
        "top_k": 40,
        "top_p": 0.95
      }
    },
    {
      "name": "Gemma 4 4B IT",
      "url": "https://huggingface.co/google/gemma-4-4b-it-gpu-int4",
      "format": "tflite",
      "backend": "gpu",
      "config": {
        "max_tokens": 2048,
        "temperature": 0.7
      }
    }
  ]
}

Programmatic on-device inference with LLM Inference API:

# gemma_edge_inference.py
from mediapipe.tasks.genai import llm_inference

# Configure the LLM
options = llm_inference.LlmInferenceOptions(
    model_path="/data/local/tmp/gemma-4-2b-it-int4.tflite",
    max_tokens=512,
    temperature=0.7,
    top_k=40,
    supported_lora_ranks=[4, 8, 16]  # Optional LoRA support
)

# Create inference engine
engine = llm_inference.LlmInference(options=options)

# Run inference
response = engine.generate_response("Explain edge computing in one sentence.")
print(response)

# Streaming inference
for chunk in engine.generate_response_async("List three benefits of on-device AI."):
    print(chunk, end="", flush=True)

Expected: AI Edge Gallery app builds and installs successfully, Gemma 4 model downloads to device, on-device inference produces coherent responses, GPU delegate activates for acceleration.

On failure: Check Android SDK version >= 26 (adb shell getprop ro.build.version.sdk), verify device has sufficient storage for model download, ensure GPU delegate is supported (adb logcat | grep -i delegate), check Hugging Face model access permissions, verify ADB connection (adb devices).

Step 3: Convert and Quantize Models with TFLite

Convert standard models to TFLite format with post-training quantization.

# convert_tflite.py
import os
import tensorflow as tf
import numpy as np

def convert_to_tflite(saved_model_path, output_path, quantization="dynamic"):
    """Convert SavedModel to TFLite with quantization."""
    converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_path)

    if quantization == "dynamic":
        converter.optimizations = [tf.lite.Optimize.DEFAULT]

    elif quantization == "int8":
        converter.optimizations = [tf.lite.Optimize.DEFAULT]
        converter.target_spec.supported_ops = [
            tf.lite.OpsSet.TFLITE_BUILTINS_INT8
        ]
        converter.inference_input_type = tf.int8
        converter.inference_output_type = tf.int8

        # Representative dataset for calibration
        def representative_dataset():
            for _ in range(100):
                yield [np.random.randn(1, 224, 224, 3).astype(np.float32)]
        converter.representative_dataset = representative_dataset

    elif quantization == "float16":
        converter.optimizations = [tf.lite.Optimize.DEFAULT]
        converter.target_spec.supported_types = [tf.float16]

    tflite_model = converter.convert()

    with open(output_path, "wb") as f:
        f.write(tflite_model)

    original_size = sum(
        os.path.getsize(os.path.join(dp, f))
        for dp, _, filenames in os.walk(saved_model_path)
        for f in filenames
    ) / (1024 * 1024)
    quantized_size = len(tflite_model) / (1024 * 1024)
    print(f"Original: {original_size:.1f} MB -> Quantized: {quantized_size:.1f} MB")
    print(f"Compression ratio: {original_size / quantized_size:.1f}x")

# Usage
convert_to_tflite("saved_model/", "model_int8.tflite", quantization="int8")

ONNX Runtime quantization alternative:

# quantize_onnx.py
from onnxruntime.quantization import quantize_dynamic, quantize_static, QuantType

# Dynamic quantization (no calibration data needed)
quantize_dynamic(
    model_input="model.onnx",
    model_output="model_int8.onnx",
    weight_type=QuantType.QInt8
)

# Static quantization (better accuracy, needs calibration)
# ... (see EXAMPLES.md for complete calibration workflow)

Expected: TFLite model generated at specified path, model size reduced by 2-4x with INT8, inference accuracy within 1-2% of original, ONNX quantization produces valid model.

On failure: Check TensorFlow version >= 2.15 for latest quantization support, verify representative dataset matches model input shape, ensure all ops are supported in TFLite (converter.allow_custom_ops = True as fallback), check ONNX opset version compatibility.

Step 4: Configure Hardware Delegates

Select and configure hardware acceleration delegates for target devices.

# configure_delegates.py
import tensorflow as tf

def create_interpreter_with_delegate(model_path, delegate="gpu"):
    """Create TFLite interpreter with hardware delegate."""

    if delegate == "gpu":
        delegate_obj = tf.lite.experimental.load_delegate(
            "libtensorflowlite_gpu_delegate.so",
            options={"precision": "fp16", "allow_quantized_models": "true"}
        )
    elif delegate == "nnapi":
        # Android Neural Networks API - routes to NPU/DSP
        delegate_obj = tf.lite.experimental.load_delegate(
            "libtensorflowlite_nnapi_delegate.so"
        )
    elif delegate == "xnnpack":
        # Optimized CPU inference
        delegate_obj = None  # XNNPACK is default in TFLite

    interpreter = tf.lite.Interpreter(
        model_path=model_path,
        experimental_delegates=[delegate_obj] if delegate_obj else None,
        num_threads=4
    )
    interpreter.allocate_tensors()
    return interpreter

Delegate selection guide:

| Device | Best Delegate | Fallback | Notes | |--------|--------------|----------|-------| | Android (Qualcomm) | NNAPI -> Hexagon DSP | GPU -> XNNPACK | Check nnapi_accelerator_name | | Android (MediaTek) | NNAPI -> APU | GPU -> XNNPACK | Dimensity chips have dedicated APU | | Android (Samsung) | NNAPI -> NPU | GPU -> XNNPACK | Exynos NPU via NNAPI | | iOS | CoreML delegate | Metal GPU | Use coreml_delegate for ANE | | Linux embedded | GPU (if available) | XNNPACK | RPi uses XNNPACK CPU | | Browser | WebGL / WebGPU | WASM SIMD | Via TensorFlow.js |

Expected: Delegate loads without errors, inference runs on target accelerator, latency improves 2-10x over CPU-only depending on model and device.

On failure: Verify delegate library exists on device, check device supports requested delegate (adb shell cat /proc/cpuinfo for CPU features), fall back to XNNPACK if GPU/NPU unavailable, check OpenCL support for GPU delegate, verify NNAPI version (adb shell getprop ro.android.ndk.version).

Step 5: Benchmark On-Device Performance

Measure inference latency, memory usage, and power consumption on target devices.

# Use TFLite benchmark tool
adb push model_int8.tflite /data/local/tmp/

# CPU benchmark
adb shell /data/local/tmp/benchmark_model \
  --graph=/data/local/tmp/model_int8.tflite \
  --num_threads=4 \
  --num_runs=50 \
  --warmup_runs=5

# GPU benchmark
adb shell /data/local/tmp/benchmark_model \
  --graph=/data/local/tmp/model_int8.tflite \
  --use_gpu=true \
  --num_runs=50

# NNAPI benchmark
adb shell /data/local/tmp/benchmark_model \
  --graph=/data/local/tmp/model_int8.tflite \
  --use_nnapi=true \
  --nnapi_accelerator_name=google-edgetpu \
  --num_runs=50

Python benchmarking:

# benchmark_edge.py
import time
import numpy as np
import psutil

def benchmark_inference(interpreter, input_data, num_runs=100):
    """Benchmark TFLite model inference."""
    input_details = interpreter.get_input_details()
    output_details = interpreter.get_output_details()

    # Warmup
    for _ in range(10):
        interpreter.set_tensor(input_details[0]["index"], input_data)
        interpreter.invoke()

    # Benchmark
    latencies = []
    mem_before = psutil.Process().memory_info().rss / (1024 * 1024)
    for _ in range(num_runs):
        start = time.perf_counter()
        interpreter.set_tensor(input_details[0]["index"], input_data)
        interpreter.invoke()
        latencies.append((time.perf_counter() - start) * 1000)
    mem_after = psutil.Process().memory_info().rss / (1024 * 1024)

    print(f"Latency (p50): {np.percentile(latencies, 50):.1f} ms")
    print(f"Latency (p95): {np.percentile(latencies, 95):.1f} ms")
    print(f"Latency (p99): {np.percentile(latencies, 99):.1f} ms")
    print(f"Memory delta: {mem_after - mem_before:.1f} MB")
    print(f"Throughput: {1000 / np.mean(latencies):.1f} inferences/sec")

Expected: Benchmark produces latency percentiles, memory usage, and throughput metrics; GPU delegate shows 2-5x speedup over CPU for vision models; Gemma 4 2B achieves 10-30 tokens/sec on flagship phones.

On failure: Ensure benchmark binary matches device architecture (arm64-v8a), verify model pushed to device (adb shell ls /data/local/tmp/), check sufficient device storage, kill background apps to reduce memory pressure, verify thermal throttling not active (adb shell cat /sys/class/thermal/thermal_zone*/temp).

Step 6: Package for Production Deployment

Build the final mobile application with embedded or downloadable model.

// Android: EdgeAIManager.kt
import com.google.mediapipe.tasks.genai.llminference.LlmInference

class EdgeAIManager(private val context: Context) {
    private var llmInference: LlmInference? = null

    fun initialize(modelPath: String) {
        val options = LlmInference.LlmInferenceOptions.builder()
            .setModelPath(modelPath)
            .setMaxTokens(512)
            .setTemperature(0.7f)
            .setTopK(40)
            .setResultListener { result, done ->
                // Handle streaming tokens
                onTokenReceived(result, done)
            }
            .build()

        llmInference = LlmInference.createFromOptions(context, options)
    }

    fun generateResponse(prompt: String): String {
        return llmInference?.generateResponse(prompt)
            ?: throw IllegalStateException("Model not initialized")
    }

    fun release() {
        llmInference?.close()
        llmInference = null
    }
}

Model download and caching strategy:

// ModelDownloader.kt
class ModelDownloader(private val context: Context) {
    private val modelDir = File(context.filesDir, "models")

    suspend fun ensureModel(modelName: String, url: String): File {
        val modelFile = File(modelDir, modelName)
        if (modelFile.exists()) return modelFile

        modelDir.mkdirs()
        // Download with progress tracking
        // ... (see EXAMPLES.md for complete implementation)
        return modelFile
    }
}

Expected: Android app builds with MediaPipe dependency, model loads on first launch, inference runs within latency budget, model cached after first download, graceful fallback when device is unsupported.

On failure: Check minSdk >= 26 in build.gradle, verify MediaPipe dependency version, ensure model file not corrupted (check SHA256), verify sufficient device storage for model, check ProGuard rules preserve MediaPipe classes, test on multiple device tiers.

Validation

• [ ] Model converts to TFLite/ONNX without op compatibility errors
• [ ] Quantized model accuracy within acceptable tolerance (< 2% degradation)
• [ ] Hardware delegate loads and accelerates inference
• [ ] Benchmark latency meets target (e.g., < 100ms for vision, < 50ms/token for LLM)
• [ ] Memory usage stays within device budget
• [ ] AI Edge Gallery successfully loads and runs Gemma 4 model
• [ ] On-device LLM generates coherent responses
• [ ] Application handles model download, caching, and updates
• [ ] Graceful degradation on unsupported devices
• [ ] Battery impact within acceptable range for target use case

Common Pitfalls

• Unsupported ops in TFLite: Custom ops fail conversion - use converter.allow_custom_ops = True or replace with supported alternatives, check op compatibility list
• Quantization accuracy loss: INT4 degrades quality for sensitive tasks - use mixed precision, calibrate with representative data, evaluate on edge-specific test set
• Delegate initialization failure: GPU delegate crashes on older devices - always implement CPU fallback, check delegate compatibility before loading
• Memory pressure on device: Model + app exceeds available RAM - use memory-mapped models, implement model unloading, reduce batch size to 1
• Thermal throttling: Sustained inference causes device overheating - implement duty cycling, reduce inference frequency, monitor thermal zones
• Model download size: Large models over cellular data - offer Wi-Fi-only download, implement resumable downloads, use progressive model loading
• Version fragmentation: Model works on some devices but not others - test on representative device matrix, use NNAPI version checks, maintain device compatibility database

Related Skills

• deploy-ml-model-serving - Cloud-based model serving (complement to edge)
• monitor-model-drift - Monitor model quality over time
• register-ml-model - Register models before edge deployment
• create-dockerfile - Containerize edge model conversion pipeline
• create-multistage-dockerfile - Multi-stage builds for model conversion pipelines