Concurrency - How to Make a System Handle Multiple Tasks Simultaneously

Imagine you’re running a model that receives many user requests at the same time. If you handle each request one by one, all users will wait a long time. Concurrency is designed to solve exactly this - making the system handle multiple requests simultaneously.

What is It Exactly?

Concurrency means the system schedules several operations at the same time, even if they’re not truly running at the exact same moment (that’s Parallelism).

Think of it like a restaurant:

• There’s one chef (GPU or CPU).
• Instead of cooking one dish from start to finish and then starting the next, they work “in cycles” - chopping vegetables for one dish while water boils for another.

Why is It Important in Inference?

When an AI model responds to many users, Concurrency determines how many active requests it can handle simultaneously. If the value is set too low - the accelerator will be idle part of the time. If it’s set too high - it will create overload, and each request will be delayed.

The goal: to find the balance between Throughput (how many requests per second) and Latency (average response time).

How Does It Work in Practice?

Servers like Triton or vLLM use smart mechanisms:

• Concurrency Slots - How many requests can be opened simultaneously.
• Dynamic Batching - Combining similar requests into one batch to better utilize the accelerator.
• Scheduler - Coordinates the flow, so the accelerator works continuously, without waste.

Bottom Line

Concurrency is one of the key tools in inference optimization: It not only makes the system run faster - it also makes better use of the hardware you already have.