Infrastructure

Throughput

Quick Answer: The number of tokens or requests an AI system can process per unit of time.

Throughput is the number of tokens or requests an AI system can process per unit of time. High throughput means handling more users or batch jobs simultaneously. Throughput is the key metric for scaling AI applications beyond prototype stage.

Example

A model serving endpoint handling 500 requests per second with an average of 200 output tokens each has a throughput of 100,000 tokens/second. Throughput can be increased through batching, model parallelism, and hardware scaling.

Why It Matters

Throughput determines whether an AI feature can scale from demo to production. Many proof-of-concept AI products fail at scale because they can't achieve the throughput needed for thousands of concurrent users.

How It Works

Throughput in AI systems measures how many requests or tokens a system can process per unit of time. For language models, it's typically measured in tokens per second (TPS) for a single request or requests per second (RPS) for the system overall.

Maximizing throughput requires different strategies than minimizing latency. Larger batch sizes increase throughput but add latency to individual requests. Continuous batching helps by dynamically grouping requests, reducing GPU idle time. Model parallelism across multiple GPUs can increase throughput linearly but adds complexity.

The throughput-cost equation drives infrastructure decisions. A single H100 GPU might serve 100 requests per second with a small model or 5 requests per second with a large model. Choosing the right model size, quantization level, and serving framework for your throughput requirements is a critical engineering decision.

Common Mistakes

Common mistake: Measuring throughput on a single request instead of under load

Single-request throughput doesn't predict system behavior under production load. Benchmark with realistic concurrent request patterns to get meaningful numbers.

Common mistake: Assuming throughput scales linearly with hardware

Doubling GPUs doesn't double throughput due to communication overhead, memory bandwidth limits, and batch size constraints. Benchmark actual scaling before purchasing hardware.

Career Relevance

Throughput engineering is essential for ML infrastructure and MLOps roles. Companies serving millions of AI requests daily need engineers who can optimize throughput while managing costs. It's also important for capacity planning and infrastructure budgeting.

Related Terms

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