AI inference throughput can be expressed as tokens, requests, sequences, or accepted tasks completed per second. It depends on hardware, memory, batching, model architecture, kernels, serving software, workload shape, and the latency targets the service must meet.
High throughput lowers unit cost when demand keeps resources utilized, but it can conflict with interactive latency. Operators balance batch size, queueing, cache use, and capacity so aggregate efficiency does not make individual users wait too long.
ELI5
Inference throughput measures how much AI work a serving system finishes in a given amount of time. It is like counting how many meals a kitchen can complete per hour, except the completed work might be tokens, requests, or model predictions.
For example, a service that produces 20,000 tokens per second has more token throughput than one producing 5,000 under the same conditions. Processing requests together can improve the total rate, but very large batches or long queues may make an individual user wait longer, so operators balance throughput with response time.
How is AI inference throughput different from speed?
Speed often describes one request's latency or token rate, while throughput measures total work completed across the serving system.
How can specialized kernels improve inference throughput?
They can execute important model operations more efficiently, reducing compute or memory overhead across many requests.
