Optimize Production with PyTorch/TF, ONNX, TensorRT & LiteRT

Machine learning frameworks, model tools, and deployment strategies have distinct use cases in a machine learning (ML) pipeline. They can exhibit strengths and weaknesses in each step of model development, training, optimization, deployment, and inference.

In this article, we compare five such tools and related technologies in detail: PyTorch, TensorFlow, LiteRT (formerly known as TensorFlow Lite), TensorRT, and ONNX. We review their features, strengths, weaknesses, and their role in a machine learning pipeline. We will also talk about how different tools work together and share common ways to deploy them, along with code snippets to help explain the concepts.

• Map tools to stages: Start with PyTorch/TF for training purposes, then use ONNX to transfer models, TensorRT for NVIDIA-GPU optimization, and deploy to mobile/edge with LiteRT.
• Optimize by specialty: TensorRT reduces latency and boosts throughput on GPUs; LiteRT shrinks binaries and memory on devices.
• Plan for production plumbing: TensorFlow offers integrated pipelines (TFX/TensorFlow Serving), while PyTorch typically combines TorchServe and ONNX Runtime/TensorRT with more glue.
• Lean on interoperability: Go PyTorch → ONNX → TensorRT for GPU serving, or TensorFlow → LiteRT for on-device apps.
• Simplified with a managed platform: DigitalOcean Gradient AI Platform can unify training and deployment across PyTorch/TF with ONNX, TensorRT, and LiteRT in one workflow.

PyTorch is an open-source deep learning framework based on a dynamic computation graph (define-by-run) and a Pythonic programming interface. PyTorch is centered on the idea of flexibility (models can be written and debugged in a natural way, as they are normal Python code).

While PyTorch is inherently dynamic, its highly optimized C++ backend and tensor libraries (such as cuDNN for GPUs) mean that it can still achieve comparable or better performance than a static graph framework. Over time, PyTorch has evolved to support research, development, and production deployment, with features such as TorchScript (a way to serialize models for optimized inference in C++ or mobile environments) and TorchServe for model serving.

Below is an overview of where PyTorch excels and where trade-offs remain.

PyTorch’s Strengths

• Pythonic API — like normal Python code, it’s easy to write and debug.
• Dynamic computation graph — accelerate experimentation and more natural debugging.
• Rich ecosystem & community — vision, NLP, tools, and libraries.
• Native Python tooling — integrates well with native Python debuggers/profilers.
• Productivity improvements — JIT and PyTorch 2.x AOT compilation for faster inference and deployment.
• GPU-first — easy to use CUDA acceleration; strong distributed/multi-GPU training and deployment.
• Deployments maturing — TorchScript, TorchServe, and Torch-TensorRT are bridging the research→production gap.

PyTorch’s Weaknesses

• Not turnkey for production (relative to TF) — PyTorch often implies a stack of TorchServe + ONNX Runtime/TensorRT + custom CI/CD, with higher glue code and ops overhead.
• Shipment hardening required — The dynamic graph nature of PyTorch requires additional scripting (TorchScript) or Ahead-of-Time compilation techniques to produce deterministic, reproducible artifacts for which parity tests are essential to avoid development→production behavior drift.
• MLOps tooling — Pytorch requires a more heterogeneous toolchain. This increases the surface that must be maintained (version drift, upgrades, security patches).
• Heavier runtime footprint — The full PyTorch runtime (CUDA/accelerator dependencies included) is quite heavy for mobile/edge use cases.

PyTorch is primarily used in the model development and training lifecycle stage. In the deployment stage, you can use PyTorch for inference. For example, running on a server (optionally wrapping the model with a web service such as Flask or using TorchServe), or converting the model to a lighter-weight representation for deployment.

PyTorch Example – Model Training Usage

In this code, we build a simple, fully connected neural network and train it using PyTorch’s nn.Module and optimization APIs. Thanks to PyTorch’s dynamic graph, we can express the training loop in Python where iteration is simply executing the forward pass of our model. After training, we can save the model’s weights. We may later either reload the model in PyTorch or export it to ONNX for use with other runtimes (We will see this later in this guide).

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
from torch.utils.data import DataLoader

# Define a simple neural network
class SimpleNet(nn.Module):
    def __init__(self):
        super(SimpleNet, self).__init__()
        self.fc1 = nn.Linear(784, 128)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(128, 10)
    def forward(self, x):
        x = x.view(-1, 784)  # Flatten images
        x = self.relu(self.fc1(x))
        return self.fc2(x)

# Prepare the training dataset and DataLoader
transform = transforms.Compose([transforms.ToTensor()])
train_dataset = datasets.MNIST(root="./data", train=True, transform=transform, download=True)
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)

# Model setup
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = SimpleNet().to(device)
optimizer = optim.Adam(model.parameters(), lr=1e-3)
criterion = nn.CrossEntropyLoss()

# Training loop
for epoch in range(5):
    for batch_x, batch_y in train_loader:
        batch_x, batch_y = batch_x.to(device), batch_y.to(device)
        optimizer.zero_grad()
        outputs = model(batch_x)
        loss = criterion(outputs, batch_y)
        loss.backward()
        optimizer.step()

print("Training completed.")
torch.save(model.state_dict(), "model_weights.pth")

TensorFlow is another popular deep learning framework, originally developed at Google. In its early versions, it introduced the concept of static computation graphs, where a model’s computation graph is first defined and then executed. This facilitated whole-model optimization and efficient deployment on diverse platforms, at the cost of some flexibility and user-friendliness. In response to community feedback (and also pressure from competition with PyTorch), TensorFlow 2.x adopted eager execution (more dynamic, similar to PyTorch) by default, while still offering users the ability to leverage the optimizations of static graphs by using the tf.function decorator and XLA compiler.

TensorFlow also supports Keras as its high-level API for model definition. It provides a rich ecosystem of tools for production. You can use it to deploy models on servers (TensorFlow Serving), on mobile/embedded devices (TensorFlow Lite, now LiteRT), in JavaScript (TensorFlow.js), or on specialized hardware (Google’s TPUs).

TensorFlow: Strengths & Weaknesses

TensorFlow is widely used in industry for large-scale training and production deployments. Its ecosystem provides an end-to-end solution, but it comes with its own trade-offs for developers.

TensorFlow’s Strengths

• Highly scalable: Built-in support for distributed training across multiple GPUs and even across machines.
• Production-ready ecosystem: TensorFlow Extended comes with components for data ingestion, data validation, and model serving for enterprise ML pipelines.
• Optimized computation: Static graph execution (when enabled) and XLA compiler can provide significant performance boosts.
• TPU integration: Native support for training and inference on Google TPUs for accelerated workloads.
• Seamless deployment: SavedModel format works with TensorFlow Serving for seamless production inference.
• Edge and mobile-ready: Conversion to LiteRT is straightforward for on-device deployment.
• Rich resources: A large community, many resources, and pre-trained models to start with for transfer learning.

TensorFlow’s Weaknesses

• Historical steep learning curve: TensorFlow 1.x required explicit graph/session management, which could be confusing for new users…
• Debugging complexity: The use of the @tf.function decorator and graph mode in TF 2.x also does not provide line-by-line execution, which can make debugging more complex.
• Heavy runtime footprint: Full TensorFlow is large and impractical for direct use on devices—hence the need for LiteRT/TFLite.
• Complex custom operations: Writing new operations or kernels requires deeper knowledge of TensorFlow internals.
• Lag in adopting research trends: Some cutting-edge layers and techniques appear in PyTorch first before being ported to TensorFlow.
• Less flexible for experimentation: Static graph behavior can feel rigid compared to PyTorch’s dynamic computation graph during rapid prototyping.

TensorFlow End-to-End: Development to Deployment

We can develop and train a model using TensorFlow, typically through the Keras API, export the model, and then deploy it.

The exported model can be used for inference in a server environment (using TensorFlow Serving or TensorFlow C++ API). For an edge (e.g., mobile, IoT) device, you can convert the model to LiteRT (TFLite) format and run inference on the device itself. TensorFlow provides integration with TensorRT to accelerate GPU inference (TF-TRT). One case reported 2.4× inference throughput on a ResNet-50 model using an NVIDIA T4 GPU over native TensorFlow GPU execution.

TensorFlow Example – Keras Model Training

In the snippet below, we build a simple feed-forward network for image classification using Keras. We compile and train the model on x_train, y_train (prepared MNIST data in this example), and save it. The SavedModel (“model_saved.keras”) can be loaded later for inference or converted for deployment. TensorFlow’s high-level API makes it unnecessary for us to construct the low-level graph. However, under the hood, it can optimize the computation graph for performance when running in production.

import tensorflow as tf

# Load MNIST dataset
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()

# Normalize the pixel values to [0, 1]
x_train = x_train / 255.0
x_test = x_test / 255.0

# Define a simple model using Keras (e.g., for MNIST classification)
model = tf.keras.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10)
])

# Compile the model with optimizer, loss, and metrics
model.compile(
    optimizer='adam',
    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    metrics=['accuracy']
)

# Train the model on training data
model.fit(x_train, y_train, epochs=5, batch_size=32)

# Evaluate on test data
test_loss, test_acc = model.evaluate(x_test, y_test, verbose=2)
print(f"\nTest accuracy: {test_acc:.4f}")

# Save the trained model to disk (SavedModel format)
model.save("model_saved.keras")

LiteRT (Lite Runtime) is essentially a lightweight inference engine that started as TensorFlow Lite. LiteRT enables pre-trained models to run on resource-constrained devices(mobile phones, tablets, IoT, edge devices, and microcontrollers).

As originally released, it was primarily designed to support TensorFlow-authored models. However, Google’s AI Edge team also extended LiteRT to work with models authored in other frameworks. It uses conversion tools that will take a model from PyTorch, JAX, or TensorFlow and convert it into the FlatBuffers .tflite format.

LiteRT: Strengths & Weaknesses

LiteRT is designed for on-device inference on mobile, embedded, and edge workloads. Here is a brief overview of where it excels—and where the trade-offs come into play:

LiteRT’s Strengths

• Ultra-lightweight runtime – super small binary (lightest build ~300 KB), important for mobile/embedded apps that need small binaries.
• Hardware acceleration support – supports Android NNAPI (DSP/NPU) and iOS Core ML, and a GPU delegate for mobile GPUs.
• Model optimizations – can apply post-training quantization (int8/float16), pruning, clustering to reduce size and latency with minimal loss in accuracy.
• Optimized for on-device performance – aggressive optimizations + delegates enable real-time inference and lower power consumption.
• Cross-platform support – supports Android, iOS, Linux (including Raspberry Pi), and microcontrollers (via LiteRT Micro).
• Offline & privacy-friendly — keeps inference local to the device, avoiding network latency and protecting user data.

LiteRT’s Weaknesses

• Inference-only runtime – no support for general on-device training (limited transfer learning support is possible in specialized scenarios).
• Operator coverage gaps — not all TensorFlow/PyTorch operators are natively supported; may need to rewrite model or create custom ops.
• Conversion friction — may require to segment graphs and apply quantization in some cases to convert model to .tflite format.
• Debugging challenges — static FlatBuffer models are harder to inspect/debug than framework-native models.
• Memory/compute limits – large transformer-scale or other memory-intensive models can still be too slow for target devices.
• Possible server offload needed — for models that exceed device compute/memory budgets, hybrid client-server inference can be required.

LiteRT in the ML Pipeline

The pipeline can be described as follows: train a model in TensorFlow/PyTorch, then convert the trained model to .tflite format using the relevant converter. Finally, deploy that .tflite file within a mobile app or on an embedded device using the LiteRT runtime.

During conversion, you can apply optimizations such as quantization or pruning. This model will then be executed as part of your software using the LiteRT interpreter (which you can use with many languages – e.g., Java/Kotlin on Android, Swift on iOS, C++ for native, or Python for rapid prototyping).

It generally has significantly better on-device performance than using a full framework runtime on-device. In a single benchmark on a Samsung S21 mobile device, a baseline image classification model performed 23 ms per inference at ~89 MB memory with TensorFlow Lite. However, the same model achieved ~31 ms (112 MB) and ~38 ms (126 MB) with ONNX Runtime and PyTorch Mobile, respectively. This highlights the importance of LiteRT’s focus on low-latency, low-memory execution on mobile devices.

LiteRT Example – Converting and Using a Model

This example shows how to convert a TensorFlow model trained in Python to LiteRT format and execute inference.

# ready MNIST → SavedModel → LiteRT(TFLite) → Inference pipeline
import tensorflow as tf
import numpy as np

print("TF version:", tf.__version__)

# 1) Load & prep MNIST
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
# Normalize to [0,1]
x_train = (x_train / 255.0).astype("float32")
x_test  = (x_test  / 255.0).astype("float32")

# 2) Define & train a simple Keras model
model = tf.keras.Sequential([
    tf.keras.layers.Input(shape=(28, 28)),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(128, activation="relu"),
    tf.keras.layers.Dense(10)  # logits
])

model.compile(optimizer="adam",
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=["accuracy"])

model.fit(x_train, y_train, epochs=2, batch_size=128, validation_split=0.1, verbose=1)

# Quick test set eval
test_loss, test_acc = model.evaluate(x_test, y_test, verbose=0)
print(f"TF model test accuracy: {test_acc:.4f}")

# 3) Export a TensorFlow SavedModel (needed for TFLite conversion)
# In TF 2.15+, prefer model.export(). If not available, fallback to tf.saved_model.save
if hasattr(model, "export"):
    model.export("model_saved")            # TF ≥ 2.15
else:
    tf.saved_model.save(model, "model_saved")  # Older TF fallback

# 4) Convert to LiteRT/TFLite (FP32 with default optimizations)
converter = tf.lite.TFLiteConverter.from_saved_model("model_saved")
converter.optimizations = [tf.lite.Optimize.DEFAULT]   # dynamic range quantization if weights permit
tflite_model = converter.convert()

with open("model_fp32.tflite", "wb") as f:
    f.write(tflite_model)
print("Wrote model_fp32.tflite")

# --- OPTIONAL: Full INT8 quantization with representative dataset ---
do_full_int8 = True
if do_full_int8:
    def rep_data():
        # Yield a few hundred samples to calibrate ranges
        for i in range(500):
            # TFLite expects a batch dimension
            yield [np.expand_dims(x_train[i], 0)]
    converter_int8 = tf.lite.TFLiteConverter.from_saved_model("model_saved")
    converter_int8.optimizations = [tf.lite.Optimize.DEFAULT]
    converter_int8.representative_dataset = rep_data
    # Force int8 I/O where supported
    converter_int8.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
    converter_int8.inference_input_type = tf.int8
    converter_int8.inference_output_type = tf.int8
    try:
        tflite_int8 = converter_int8.convert()
        with open("model_int8.tflite", "wb") as f:
            f.write(tflite_int8)
        print("Wrote model_int8.tflite")
    except Exception as e:
        print("INT8 conversion fell back / failed:", e)
        tflite_int8 = None

# 5) Run inference with the TFLite Interpreter (FP32 model)
import tensorflow.lite as tflite

def tflite_predict(tflite_path, image_28x28):
    interpreter = tflite.Interpreter(model_path=tflite_path)
    interpreter.allocate_tensors()
    in_details = interpreter.get_input_details()
    out_details = interpreter.get_output_details()

    inp = image_28x28
    # Match dtype & shape expected by the model
    if in_details[0]["dtype"] == np.float32:
        inp = inp.astype(np.float32)
    elif in_details[0]["dtype"] == np.int8:
        # Quantized model expects int8; apply quantization params
        scale, zero_point = in_details[0]["quantization"]
        if scale == 0:
            # Safety: if no scale provided (rare), just cast
            inp = inp.astype(np.int8)
        else:
            inp = (inp / scale + zero_point).round().astype(np.int8)

    # Add batch dimension
    inp = np.expand_dims(inp, 0)

    interpreter.set_tensor(in_details[0]["index"], inp)
    interpreter.invoke()
    out = interpreter.get_tensor(out_details[0]["index"])

    # If output is int8, dequantize back to float for softmax/argmax
    if out_details[0]["dtype"] == np.int8:
        scale, zero_point = out_details[0]["quantization"]
        if scale != 0:
            out = (out.astype(np.float32) - zero_point) * scale

    # Convert logits to probabilities and pick class
    probs = tf.nn.softmax(out, axis=-1).numpy()[0]
    pred  = int(np.argmax(probs))
    conf  = float(probs[pred])
    return pred, conf

# Test on a few MNIST samples with FP32 model
for idx in [0, 1, 2]:
    pred, conf = tflite_predict("model_fp32.tflite", x_test[idx])
    print(f"[FP32] Sample {idx}: pred={pred}, conf={conf:.3f}, true={y_test[idx]}")

# If INT8 model exists, test it as well
if 'tflite_int8' in locals() and tflite_int8 is not None:
    for idx in [0, 1, 2]:
        pred, conf = tflite_predict("model_int8.tflite", x_test[idx])
        print(f"[INT8] Sample {idx}: pred={pred}, conf={conf:.3f}, true={y_test[idx]}")

The code first loads and normalizes the MNIST dataset, defines and trains a small fully connected network, and evaluates accuracy. The trained model is then saved as a SavedModel, which is the input to the TFLiteConverter. The converter generates two models, the default FP32 LiteRT model and (optionally) a fully quantized INT8 model using a representative dataset to calibrate ranges. Finally, the code defines a helper function tflite_predict() which loads a .tflite file, prepares and quantizes/dequantizes data as needed, executes inference, and returns the predicted digit and confidence. A few test samples are passed through both the FP32 and INT8 models to confirm correct deployment and show example outputs.

NVIDIA TensorRT is an SDK and runtime for low-latency and high-throughput deployment of neural networks on NVIDIA GPUs. TensorRT can be thought of as a deep learning model compiler: you supply a trained model (usually in ONNX or a framework-specific format) and it performs a series of optimizations to output an optimized inference engine that runs on the GPU.

These optimizations include**:**

• Layer fusion — combine compatible ops to reduce memory traffic and kernel launches.
• Kernel auto-tuning —select the fastest CUDA kernels for a given shape/hardware.
• Memory planning — optimize tensor lifetimes and workspace to minimize copies/peaks.
• Reduced precision — enable FP16 and INT8 (with calibration/QAT) support for significant speedups and reduced bandwidth requirements.
• Dynamic shapes & profile caching — build execution profiles for shape ranges to avoid re-optimization at runtime.

The final output is a highly optimized binary that can execute the model’s forward pass significantly faster than standard implementations.

TensorRT: Strengths & Weaknesses

This section details TensorRT’s standout features and the trade-offs you’ll need to manage.

TensorRT’s Strengths

• High GPU performance – Leverages FP16/INT8 precision and NVIDIA-architecture specific optimizations to accelerate inference (execution) dramatically (often up to ~40× faster than CPU-based inference).
• GPU-level efficiency – Can achieve 2–5× lower latency or higher throughput than unoptimized TensorFlow/PyTorch running on a GPU.
• Batching/concurrency – Designed for large-scale inference servers (many simultaneous requests) where throughput matters.
• Flexible integration – Can be integrated as a standalone or as part of Triton Inference Server or ONNX Runtime (used as an execution provider) or as part of TensorFlow integration.
• Extensive ecosystem support – Integrates with NVIDIA SDKs like DeepStream (video analytics), Riva (speech/AI assistants), and more to power end-to-end solutions.
• Language support – Supports Python and C++ APIs to integrate into customized data processing pipelines.

TensorRT’s Weaknesses

• Depends on NVIDIA hardware – TensorRT runs only on NVIDIA GPUs. Inference solutions built on TensorRT are not portable to CPUs and other accelerators.
• Conversion complexity – model must be converted into a TensorRT engine format, which might require refactoring for unsupported operations or writing custom plugins.
• Tuning effort required – For optimal results, you must manually tune various aspects, such as workspace size, precision mode, and input shape profiles.
• Hardware-specific engines – TensorRT engines are optimized to a specific GPU family and architecture. You can rebuild them for other hardware if porting your application (e.g., from a datacenter GPU to a Jetson edge device).
• Rebuild on model updates – Any model change requires re-conversion and re-optimization to maintain performance.
• Deployment complexity – Development and maintenance effort is higher relative to integrated solutions like TF Serving or ONNX Runtime-only.

TensorRT in the Pipeline: Inference Only

TensorRT is only for inference-time. For example, you train a model using PyTorch or TensorFlow on a GPU, then export it to ONNX (TensorRT supports ONNX as a standard input format). You can then call TensorRT APIs or utilities to deploy that ONNX model, build an engine (calibrating with sample data, if using int8 quantization, for INT8), and run that engine in your application.

Now load the engine on the C++ server app or Python bindings (if you have a small-scale setup). If you’re in a high-throughput server case (think serving millions of queries in production), it’s very common for TensorRT engines to be run in NVIDIA’s Triton Inference Server, which handles multiple models and concurrency.

TensorRT Example – Converting an ONNX Model to a TensorRT Engine

Below is a simplified (pseudo-code) example using TensorRT’s Python API for building an engine from an ONNX model and performing inference. This provides an idea of the overall workflow, without API details.

import tensorrt as trt

onnx_file = "model.onnx"
engine_file = "model.plan"  # TensorRT engine file

# Set up TensorRT logger and builder
logger = trt.Logger(trt.Logger.INFO)
builder = trt.Builder(logger)
network = builder.create_network(flags=1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
parser = trt.OnnxParser(network, logger)

# Parse the ONNX model to populate the TensorRT network
with open(onnx_file, "rb") as f:
    parser.parse(f.read())
# (In practice, check parser.error for unsupported ops here.)

# Configure builder
builder.max_batch_size = 1
config = builder.create_builder_config()
config.max_workspace_size = 1 << 30  # 1GB workspace for optimization
config.flags |= trt.BuilderFlag.FP16  # enable FP16 precision if supported

# Build the TensorRT engine
engine = builder.build_engine(network, config)
with open(engine_file, "wb") as f:
    f.write(engine.serialize())  # save the engine to file

# Use the engine for inference
runtime = trt.Runtime(logger)
with open(engine_file, "rb") as f:
    engine_bytes = f.read()
engine = runtime.deserialize_cuda_engine(engine_bytes)
context = engine.create_execution_context()

# Assuming a single input and single output for simplicity
input_shape = engine.get_binding_shape(0)
output_shape = engine.get_binding_shape(1)
# Allocate device memory for inputs and outputs (using PyCUDA or similar)
# ... (omitted for brevity)
# Execute inference
context.execute_v2(bindings=[d_input_ptr, d_output_ptr])
# Copy results from device memory to host and use the output

The code above outlines the steps required to convert an ONNX model to a TensorRT engine. We start by constructing a Builder and an OnnxParser, which are used to read the model graph. Next, some builder configuration is set (such as the workspace size and enabling FP16 support). The build_engine call performs the TensorRT optimization passes and outputs an engine (serialized to “model.plan”). We can later deserialize the engine and create an execution context that can be used to run inference.

Let’s consider the steps required to use TensorRT for inference:

• Parse the Model: Load and interpret the model structure using TensorRT’s IParser or other relevant tools.
• Build Optimized Engine: Create an optimized engine from the parsed model that is specific to the target hardware. This usually involves optimizations such as layer fusion and precision calibration.
• Run Inference: Perform inference using the optimized engine. This step involves managing device memory for inputs and outputs and calling the execute_v2 method on the execution context.

In real applications, one would also handle dynamic shapes or multiple bindings, but this requires a much deeper dive into TensorRT code. In practice, most developers use a high-level wrapper library or ONNX Runtime with TensorRT backend to avoid writing the low-level code themselves.

ONNX (Open Neural Network Exchange) is not a training framework; it is an open format for representing ML models that provides a runtime (ONNX Runtime) for executing models.

You can train a model in one framework (e.g., PyTorch or TensorFlow), export it to ONNX format (a computational graph with standard operations), then run it using another tool or even a different hardware backend. The decoupling of frameworks from runtime is very powerful in a production setting, where you can choose the best framework for development, then pick the best runtime for deployment.

ONNX: Strengths & Weaknesses

The summary below covers areas where the stack excels and its current limitations.

Strengths (ONNX / ONNX Runtime)

• Flexibility & interoperability: Train in one framework (e.g., PyTorch), deploy with another (e.g., ORT in C++).
• Lightweight, inference-focused runtime: Smaller install size than full frameworks. No training overhead.
• Cross-platform: Windows, Linux, macOS, and mobile (ORT Mobile with reduced ops).
• Strong performance: On-par with or better than native framework inference, especially on CPU; with some batch-1 GPU runs, ORT can be faster after graph optimizations (e.g., ~24.2 ms vs ~30.4 ms on ResNet-50).
• Proven at scale: Reportedly outperforms TorchScript on some Microsoft production workloads.
• Healthy ecosystem: A healthy ecosystem of converters, model zoos, and tools (Netron, onnxoptimizer, etc.).

Weaknesses (ONNX / ONNX Runtime)

• Conversion complexity: Export can fail or result in a performance drop if the model contains ops that aren’t yet standardized/supported.
• Gaps in custom ops: Models with non-standard layers may require fallbacks or custom plugins. These can become “black boxes.”
• Harder debugging: Static ONNX graphs are difficult to debug as they are disconnected from the original framework, source code, and tooling.
• Less “batteries-included”: Pre/post-processing, and other pipeline “glue” is often performed outside ORT.
• Workflow overhead: Adds an extra export/validation step into the workflow that must be maintained.

ONNX as the Handoff Layer: Train Anywhere, Deploy Everywhere

ONNX is often in the middle of the pipeline. A common scenario would be to train in PyTorch and then use torch.onnx.export to export the model to model.onnx. We can then take that ONNX model and deploy it in a production service (using ONNX Runtime that may be written in C++ for efficiency, or in Python if that’s suitable).

Let’s consider that you are working with a TensorFlow model but would like to use TensorRT without going through TensorFlow’s integration. In that case, you could convert the TF model to ONNX and then hand it to TensorRT (since TensorRT also has native ONNX support).

Similarly, ONNX is used in model compression and quantization workflows. For example, you can export a model to ONNX and then perform post-training quantization on it via the tooling provided by ONNX Runtime.

End-to-end interoperability may be among the most crucial factors for expert users. None of the tools mentioned in our chart will be used in isolation in a real-world end-to-end pipeline. Select two or three tools from this list and use them in combination to meet training and deployment needs. In the following, we describe some typical use-cases and workflows, and how the pieces come together:

PyTorch and TensorFlow are the “front ends”, used to build the models; ONNX is a common “in-between” format to transfer models from one framework to the other; TensorRT and LiteRT are “end points”, each optimized for particular hardware (GPUs and edge devices, respectively).

• PyTorch vs TensorFlow—when should I pick each? PyTorch is great for fast research iteration and Pythonic debugging; TensorFlow is better for end-to-end, production-grade ML pipelines (TFX, TF-Serving, TPU) and smoother enterprise operations.

• What is LiteRT (formerly TFLite), and when do I use it? LiteRT is a lightweight, on-device inference runtime built for mobile/edge. Train your model with TensorFlow or PyTorch, convert to .tflite, and run with hardware delegates (NNAPI, Core ML, GPU) for low-latency, low-power inference.

• How do ONNX and TensorRT work together? Export your trained model to ONNX, then use TensorRT to compile it into a highly optimized engine for NVIDIA GPUs (FP16/INT8, kernel fusion). ONNX is the bridge; TensorRT is the GPU turbocharger.

Choose tools based on where you run and how you scale: PyTorch or TensorFlow to iterate and train; ONNX to decouple training and serving; TensorRT for best NVIDIA-GPU performance; and LiteRT for small, low-latency, on-device inference. Most winning stacks are multi-framework (e.g., PyTorch → ONNX → TensorRT for GPU serving or TensorFlow → LiteRT for mobile) with a single exported artifact you can benchmark and ship.

A practical way to achieve this is through the DigitalOcean Gradient AI Platform. Boot up managed GPU notebooks, train, and deploy accelerated endpoints without managing infrastructure. This will enable you to use PyTorch/TensorFlow with ONNX, TensorRT, or LiteRT, all in a single streamlined workflow.

• A Comparative Survey of PyTorch vs TensorFlow for Deep Learning: Usability, Performance, and Deployment Trade-offs
• Leveraging TensorFlow-TensorRT integration for low-latency Inference
• LiteRT overview
• TensorFlow Lite vs PyTorch Mobile for On-Device Machine Learning
• Edge AI: TensorFlow Lite vs. ONNX Runtime vs. PyTorch Mobile
• ONNX, ONNX Runtime, and TensortRT
• ONNX vs PyTorch Speed: In-Depth Performance Comparison
• Scaling-up PyTorch inference: Serving billions of daily NLP inferences with ONNX Runtime