Model Export - Multimodal AI

Source Model

Model

Component

Export Format

ONNX

Cross-platform, ONNX Runtime

TorchScript

PyTorch deployment

TensorRT

NVIDIA GPU optimized

Core ML

Apple devices

TensorFlow Lite

Mobile / Edge

Optimization

Quantization (INT8) Pruning Layer Fusion FP16 Mode

Export Summary

1.71 GB

Original

428 MB

Exported

Compression

2.3x

Speedup

Export Progress 0%

Ready to export

Format Comparison

Format	Size	Latency	Acc Drop	Platforms
PyTorch (FP32)	1.71 GB	45 ms	0%	Python
ONNX	1.71 GB	38 ms	0%	Cross-platform
ONNX + INT8	428 MB	19 ms	0.5%	Cross-platform
TensorRT FP16	856 MB	12 ms	0.1%	NVIDIA GPU
TensorRT INT8	428 MB	8 ms	0.8%	NVIDIA GPU
Core ML	856 MB	22 ms	0.1%	Apple
TFLite	428 MB	85 ms	0.6%	Mobile

Usage Example

import onnxruntime as ort
import numpy as np

# Load exported ONNX model
session = ort.InferenceSession("clip_vit_l14.onnx")

# Prepare inputs
image = preprocess_image(image_path)  # [1, 3, 224, 224]
text = tokenize(["a photo of a cat"])  # [1, 77]

# Run inference
outputs = session.run(
    None,
    {
        "image": image.numpy(),
        "text": text.numpy()
    }
)

image_features, text_features = outputs
similarity = (image_features @ text_features.T).softmax(dim=-1)

Export Log

[INFO] Ready to export