Why Isn’t Your Model Running as Fast as Expected? Bottlenecks in Inference

Have you heard the term “bottleneck”? In any computational system, there’s always one stage that limits everything else - just like a water bottle, where you can only pour as fast as the neck allows.

In the world of Inference (running trained models to generate answers), this happens all the time.

1. Memory Bottleneck

The model stores temporary information in memory - for example, what it has already computed in a previous step. When it needs to access this information again, it “requests” it from the main memory (RAM or DRAM). If the path to memory is long or slow, the model simply waits. Meanwhile, the processing units do nothing - wasting precious time.

Conclusion: When memory is too slow, even a powerful computer seems slow.

2. Compute Bottleneck

Sometimes the opposite problem occurs - memory is available, but the computation itself is too heavy. This is where the accelerator comes in - a specialized hardware component, like a GPU or NPU, designed specifically to handle large, parallel mathematical computations, instead of the regular CPU. But even the accelerator is limited by the number of operations it can perform per second. If the model demands more computation than the accelerator can handle, a slowdown occurs.

Conclusion: The accelerator is meant to speed up the process, but it also needs to match the size and type of the model.

3. I/O Bottleneck

Sometimes everything is fast - but the data “travels” too slowly between components. For example, when the accelerator has to wait for the CPU to finish transferring new input. Every time such a data transfer occurs, a small delay is introduced. When these delays add up, the entire system appears slow.

Conclusion: Data must flow continuously, without waiting between components.

4. Scheduling Bottleneck

Even if all components are fast, sometimes the way tasks are divided causes unnecessary load. The system schedules its runs using streams - computation flows that run in parallel. If the scheduling is unbalanced, some streams will wait for others to finish.

Conclusion: Proper management of streams is almost as important as the hardware’s power itself.

Summary

An inference system is like a chain - its strength is determined by its weakest link. Understanding where the bottleneck is located is the first step to real optimization: Is it in memory, computation, communication, or scheduling?

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In the next post, we’ll learn about: Practical optimization techniques - how to transform a slow and expensive model into a fast and efficient system using Quantization, Batching, and other tools.