Why Do We Need to Understand Hardware for Inference Optimization?

When discussing inference optimization, many immediately think of code: improving the model, using a faster library, or tweaking the batch size. However, a significant portion of performance - often the vast majority - doesn’t depend on the code at all, but on how the system itself is built and managed at the hardware level.

The Stage After Training

Once you’ve trained a model, it’s ready to make predictions (inference). The question now is - how do you run it efficiently?

A model is just a collection of computations. But behind every prediction lies an entire system: processors, memory, communication between components, process management - all of which directly impact two critical metrics:

• Latency - How long it takes to return an answer.
• Throughput (TPS) - How many predictions can be processed per second.

Where Does Performance “Leak”?

If you don’t understand how the system works, it’s easy to end up in a situation where:

• The processor is only busy part of the time.
• Memory is transferred between components inefficiently.
• Multiple processes compete for the same resources.

In other words - the model’s performance isn’t just measured by its computational capabilities, but by how intelligently the system around it is managed.

What Will We Learn Next?

In the upcoming posts, we’ll dive into the components behind the scenes of inference performance:

• NUMA - Why not all memory in a computer is equally accessible.
• Core Allocation - How proper CPU resource management improves stability and performance.
• Divided Resources - How to distribute loads between models and processes.
• Finally - how all of this converges into tangible metrics like latency and throughput.

Conclusion

Good optimization doesn’t just start with efficient code - it starts with a deep understanding of how the hardware “thinks.” Those who understand this can extract performance from the system that feels almost magical - but is actually the result of smart engineering insight.