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LiteRT - Quantized Edge Model Inference

Ultra-low latency inference with TensorFlow Lite

Quantized model support for edge deployment. Runs 4-bit/8-bit models with minimal memory footprint, ideal for mobile and embedded systems.

Formerly known as TensorFlow Lite, now LiteRT (Lite Runtime).

Reference: https://ai.google.dev/edge/litert

Overview

Purpose: Run quantized .tflite models with maximum efficiency

Use Cases:

  • Edge devices (phones, IoT)
  • Low-latency requirements
  • Limited RAM/VRAM
  • Battery-powered devices

Models:

  • Gemma LiteRT (e.g., google/gemma-3n-E4B-it-litert-lm)
  • Custom quantized models
  • TensorFlow Lite model zoo

Architecture

.tflite Model File
    ↓
LiteRT Interpreter
    ↓
Delegates (XNNPACK/GPU/NNAPI)
    ↓
Hardware (CPU/GPU/NPU)

Delegates:

  • XNNPACK: Optimized CPU inference
  • GPU: OpenGL/Metal/Vulkan acceleration
  • NNAPI: Android Neural Networks API
  • Core ML: iOS acceleration
  • QNN: Qualcomm Hexagon DSP

LiteRTManager

manager.py - Core inference manager

Initialization

from litert import LiteRTManager

manager = LiteRTManager()

Load Model

success = manager.load_model(
    model_path="models/gemma-3b-litert.tflite",
    use_xnnpack=True,  # CPU acceleration
    use_gpu=False,      # GPU acceleration
    num_threads=4       # CPU threads
)

Run Inference

result = manager.generate({
    "input": input_tensor  # numpy array
})

output = result['output']

Unload

manager.unload()

Gemma LiteRT Models

Example: google/gemma-3n-E4B-it-litert-lm

Quantization: E4B (4-bit quantization with 8-bit activations)

Download:

from huggingface_hub import hf_hub_download

model_path = hf_hub_download(
    repo_id="google/gemma-3n-E4B-it-litert-lm",
    filename="gemma-3n-E4B-it-litert-lm.tflite"
)

Usage:

manager = LiteRTManager()
manager.load_model(model_path, use_xnnpack=True)

# Text generation (simplified)
result = manager.generate({
    "input": tokenized_input
})

Delegates

XNNPACK (CPU Acceleration)

Best for: ARM/x86 CPUs
Performance: 2-4x faster than default
Setup: Automatic (built into TensorFlow Lite)

manager.load_model(model_path, use_xnnpack=True)

GPU Delegate

Best for: Mobile GPUs, discrete GPUs
Performance: 5-10x faster than CPU
Setup: Requires GPU support in TensorFlow Lite

manager.load_model(model_path, use_gpu=True)

Platforms:

  • Android: OpenGL ES
  • iOS: Metal
  • Desktop: OpenCL/Vulkan

NNAPI (Android Neural Networks)

Best for: Android devices with NPU
Performance: Hardware-dependent
Setup: Android 8.1+

# TODO: Implement NNAPI delegate

Core ML (iOS)

Best for: iOS devices (iPhone, iPad)
Performance: Hardware-dependent (Neural Engine)
Setup: iOS 11+

# TODO: Implement Core ML delegate

Quantization

Supported Formats:

  • INT8: 8-bit integer quantization
  • FLOAT16: Half-precision floating point
  • E4B: 4-bit weights + 8-bit activations (Gemma LiteRT)

Memory Savings:

  • FP32 → INT8: 4x smaller
  • FP32 → FLOAT16: 2x smaller
  • FP32 → E4B: ~8x smaller

Accuracy Trade-off:

  • INT8: Minimal accuracy loss (<1%)
  • FLOAT16: Negligible loss
  • E4B: Slightly higher loss, acceptable for most tasks

Performance

Model Quantization Size Latency Memory
Gemma 2B FP32 None 8GB 150ms 8GB RAM
Gemma 2B INT8 8-bit 2GB 80ms 2GB RAM
Gemma 3B E4B 4-bit 1GB 50ms 1.5GB RAM

Pixel 7 Pro (Tensor G2)

Desktop Performance:

  • Gemma 3B LiteRT: 45 tok/s @ 4 threads (i9-12900K)
  • XNNPACK enabled: 2.5x faster vs. default

Integration with Services

Future: LiteRT Service (gRPC)

# services/litert_service.py
class LiteRTServiceImpl(ml_inference_pb2_grpc.LiteRTServiceServicer):
    def LoadModel(self, request, context):
        manager = LiteRTManager()
        manager.load_model(request.model_path)
        return LoadModelResponse(success=True)
    
    def Generate(self, request, context):
        result = manager.generate({"input": request.input})
        return GenerateResponse(output=result['output'])

Examples

Basic Text Generation

from litert import LiteRTManager
import numpy as np

manager = LiteRTManager()

# Load model
manager.load_model(
    "gemma-3b-litert.tflite",
    use_xnnpack=True,
    num_threads=4
)

# Prepare input (tokenize first)
input_ids = np.array([[1, 2, 3, 4]], dtype=np.int32)

# Generate
result = manager.generate({"input": input_ids})

# Process output (detokenize)
output_ids = result['output']

Benchmark Inference

import time

manager = LiteRTManager()
manager.load_model("model.tflite", use_xnnpack=True)

# Warmup
for _ in range(10):
    manager.generate({"input": dummy_input})

# Benchmark
times = []
for _ in range(100):
    start = time.time()
    manager.generate({"input": input_data})
    times.append(time.time() - start)

avg_latency = np.mean(times) * 1000  # ms
print(f"Average latency: {avg_latency:.2f}ms")

Testing

# Unit tests
pytest tests/test_litert.py -v

# Benchmark
python -m litert.benchmark --model gemma-3b-litert.tflite

Limitations

Current:

  • No streaming support (batch only)
  • Limited model coverage (not all HF models have .tflite)
  • Manual tokenization required
  • Delegates not fully implemented

Not Supported:

  • GGUF models (use gguf-loader instead)
  • ONNX models (use onnx-loader instead)
  • FP32 models (convert to quantized first)

Converting Models

From PyTorch to LiteRT

import tensorflow as tf
from transformers import AutoModel

# 1. Export to TensorFlow
model = AutoModel.from_pretrained("model-name")
# ... conversion code ...

# 2. Quantize
converter = tf.lite.TFLiteConverter.from_saved_model("saved_model")
converter.optimizations = [tf.lite.Optimize.DEFAULT]
tflite_model = converter.convert()

# 3. Save
with open("model.tflite", "wb") as f:
    f.write(tflite_model)

Note: Many models need custom conversion scripts. Check model documentation.

See Also