Parallelism - How to Make a Computer “Think” in Multiple Directions Simultaneously

When we run an AI model, it needs to process a lot of mathematical computations. To make this happen quickly - we want different parts of the computation to work in parallel, not one after the other. This is exactly the meaning of Parallelism.

What’s the Difference Between Parallelism and Concurrency?

• Concurrency = How many tasks are “open” at the same time, even if the processor switches between them intermittently.
• Parallelism = How many tasks are actually running simultaneously, on different cores or chips.

Imagine a kitchen:

• In concurrency - one chef switches between several pots.
• In parallelism - multiple chefs cook together, each on a different pot.

Why is It Important in Inference?

When running a large model, the work can be divided among several processors or accelerators:

• One part computes the model’s initial layers.
• Another part computes other layers in parallel.
• Finally, the results are combined.

This way, you can achieve shorter response times without compromising the model’s accuracy.

Main Types of Parallelism

Data Parallelism

Each accelerator gets a copy of the model but works on different data. For example: One GPU processes image A, another GPU processes image B.

Model Parallelism

The model itself is split across multiple accelerators. One GPU computes part of the layers, another GPU continues from where the first left off.

Pipeline Parallelism

The model’s layers flow like an “assembly line”: While one device computes one stage, another is already working on the next stage.

Bottom Line

Parallelism is one of the key ways to make AI systems run faster and at a larger scale. As models grow larger - Parallelism becomes increasingly critical for performance.