What is Inference Benchmarking - and Why is it So Important?

Before diving deep into Docker, Containers, and performance measurement tools - it’s essential to understand what exactly we’re measuring and why it’s critical.

What is Inference?

Artificial Intelligence consists of two main stages:

• Training - The learning phase, where the model “understands the world.”
• Inference - The usage phase, where it receives input and returns a result.

For example: A model that identifies a cat in an image - During training, it learns what a cat is, and during inference, it sees a new image and returns “this is a cat.”

So What is Benchmarking?

Benchmarking is the process of testing and evaluating the performance of a system - in our case, a model during inference. It includes metrics such as:

• Latency - How long does it take for the model to return an answer?
• Throughput - How many samples per second can it process?
• Resource Efficiency - How much CPU, GPU, and memory are consumed to achieve this result?

Why is it Important?

In the real world, a model doesn’t live inside a Jupyter Notebook. It runs on a real server, needs to respond to users in real-time, and handle varying loads - without burning expensive resources.

Without accurate benchmarking:

• You can’t tell if an upgrade improved or worsened performance,
• It’s hard to choose the right inference engine or hardware,
• And most importantly - it’s difficult to reliably reproduce results.

Conclusion

Benchmarking isn’t just about measuring speed. It’s about building trust - knowing that the model, infrastructure, and optimizations work together in harmony. It’s the cornerstone of any efficient AI system at scale.