Accelerating AI Inference: Implementing ONNX Runtime on KVM Infrastructure

It is 2019, and we have a problem. You have spent weeks training a ResNet-50 or a custom NLP model in PyTorch. Your validation loss is beautiful. Your data scientists are celebrating. But now you have to deploy it.

Suddenly, the reality of the Global Interpreter Lock (GIL) hits you. Running heavy inference loops in raw Python on a standard web server is a recipe for latency spikes. If you are serving customers in Oslo or Bergen, a 500ms delay is noticeable. A 2-second delay is unacceptable.

Until recently, your options were grim: rewrite the model in C++ (painful), use TensorFlow Serving (heavy), or throw expensive hardware at the problem. But with the recent maturity of the Open Neural Network Exchange (ONNX) and Microsoft's cross-platform ONNX Runtime, we finally have a performance-obsessed path forward.

The Inference Bottleneck

When you run a model in a development environment, you rarely care about single-request latency. In production, however, CPU cycles are money. Most "cloud" providers oversell their CPU cores. If you try to run matrix multiplications on a noisy shared vCPU, your inference time will jitter unpredictably. This is why we insist on KVM virtualization at CoolVDS—you need guaranteed instruction sets (like AVX2) and dedicated time slices.

ONNX Runtime bypasses the Python interpreter's overhead for the heavy lifting, executing the computation graph directly in optimized C++. This isn't just a 5% gain; in our benchmarks on local NVMe instances, we see throughput improvements of 2x to 3x compared to raw PyTorch 1.0 execution.

Step 1: Exporting from PyTorch 1.0

With the release of PyTorch 1.0 late last year, exporting to ONNX has become a first-class citizen. You do not need external converters anymore. Here is how you take a standard model and freeze it into the .onnx format. This example assumes you are running PyTorch 1.0.1.

import torch
import torchvision

# Load a pre-trained model (e.g., ResNet18)
model = torchvision.models.resnet18(pretrained=True)
model.eval()

# Create a dummy input that matches your input dimensions
# (Batch Size, Channels, Height, Width)
dummy_input = torch.randn(1, 3, 224, 224)

# Export the model
torch.onnx.export(model,               # model being run
                  dummy_input,         # model input (or a tuple for multiple inputs)
                  "resnet18.onnx",     # where to save the model
                  export_params=True,  # store the trained parameter weights inside the model file
                  opset_version=10,    # the ONNX version to export the model to
                  do_constant_folding=True,  # whether to execute constant folding for optimization
                  input_names = ['input'],   # the model's input names
                  output_names = ['output'], # the model's output names
                  dynamic_axes={'input' : {0 : 'batch_size'},    # variable length axes
                                'output' : {0 : 'batch_size'}})

print("Model exported to resnet18.onnx")

This generates a serialized protobuf file. This file is framework-agnostic. You can load it in C#, Java, or C++, but for this guide, we will stick to a highly optimized Python wrapper utilizing the ONNX Runtime backend.

Step 2: High-Performance Inference

To run this, you need the onnxruntime library. Unlike full deep learning frameworks which can bloat your Docker images by gigabytes, the runtime is relatively lightweight.

pip install onnxruntime numpy

Now, let’s look at the inference code. Notice how we strip away the PyTorch dependency entirely for the execution phase. This reduces the memory footprint on your VPS significantly.

import onnxruntime as ort
import numpy as np
import time

# Initialize the inference session
# This pre-loads the model and optimizes the graph for the specific CPU architecture
session = ort.InferenceSession("resnet18.onnx")

# Generate random data for testing (simulating an image)
# Note: ONNX Runtime expects numpy arrays, not Tensors
input_data = np.random.randn(1, 3, 224, 224).astype(np.float32)

start_time = time.time()

# Run inference
input_name = session.get_inputs()[0].name
outputs = session.run(None, {input_name: input_data})

end_time = time.time()

print(f"Inference time: {(end_time - start_time) * 1000:.2f} ms")
print(f"Output shape: {outputs[0].shape}")

The Infrastructure Reality Check

Code optimization can only take you so far. If your disk I/O is slow, loading the model (which can be hundreds of megabytes) into memory will delay your application startup or auto-scaling triggers. If your network has high jitter, the fast inference time is wasted.

Pro Tip: When running ML inference on CPU, disable CPU power saving modes on your host if possible, or choose a provider that handles this. On CoolVDS, our compute nodes are tuned for performance, preventing the CPU from downclocking aggressively during idle micro-seconds.

We see many developers in Europe deploying these models on budget shared hosting, only to find that their InferenceSession initialization times out or fluctuates wildly. For reliable AI in production, you need:

• NVMe Storage: For near-instant reading of large model weights.
• Dedicated RAM: Swapping to disk during matrix multiplication is a death sentence for performance.
• Data Sovereignty: If your model processes PII (Personally Identifiable Information) from Norwegian citizens, keeping the compute within Norway (Datatilsynet jurisdiction) simplifies your GDPR compliance significantly compared to US-based clouds.

Containerization for Deployment

To ensure this runs identically on your local machine and your remote CoolVDS instance, we wrap it in Docker. This Dockerfile is optimized for size and build speed, using Python 3.6-slim.

FROM python:3.6-slim

# Install system dependencies if needed (e.g. for image processing)
RUN apt-get update && apt-get install -y --no-install-recommends \
    libgomp1 \
    && rm -rf /var/lib/apt/lists/*

WORKDIR /app

# Copy requirements first to leverage Docker cache
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY resnet18.onnx .
COPY inference.py .

CMD ["python", "inference.py"]

Why This Matters Now

The gap between "Research AI" and "Production AI" is closing. Tools like ONNX Runtime are the bridge. However, a bridge needs a solid foundation. You cannot build high-frequency inference services on unstable I/O.

By combining the software efficiency of ONNX with the hardware reliability of CoolVDS's NVMe-backed KVM instances, you achieve a sweet spot: low latency without the massive cost of dedicated GPU instances for every single inference task.

Do not let your infrastructure be the reason your model fails in the real world. Test your inference speeds on a platform designed for raw performance.

Ready to bench? Deploy a high-frequency compute instance on CoolVDS today and see the millisecond difference.