Inference
Tokens per Second
A throughput measure of how many output tokens a model or serving system produces each second.
In depth
A throughput measure of how many output tokens a model or serving system produces each second. Per-user tokens per second describes how fast a single response streams and approaches the inverse of inter-token latency on long outputs, while system tokens per second totals output across all concurrent requests. The two pull against each other: more concurrency raises system throughput but slows each individual stream, so check which one a benchmark reports.
Primary source: docs.nvidia.com
Example
In practice, developers reach for Tokens per Second when they need the capability described above as part of an AI feature or workflow.
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Hands-on guides, comparisons, and tutorials that cover Inference.
FAQ
Common questions
What is Tokens per Second?
A throughput measure of how many output tokens a model or serving system produces each second.
Why does Tokens per Second matter for AI developers?
Tokens per Second sits in the Inference part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Tokens per Second?
Developers Digest publishes tutorials and videos that cover Inference topics including Tokens per Second. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
Inference
Top-K / Top-P Sampling
Two methods for controlling the randomness of model output during token generation.
Inference
Thinking Tokens
The tokens a reasoning model generates while working through a problem before it writes its final answer.
Inference
Reasoning Effort
An API setting that tells a reasoning model how much work to put into a response, trading answer quality against latency and token cost.
Put this concept to work
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