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The Pareto frontier, represented in the figure below, helps visualize the most optimal ways to balance trade-offs between competing goals — like faster responses vs. serving more users simultaneously — when deploying AI at scale. The vertical axis represents throughput efficiency, measured in tokens per second (TPS), for a given amount of energy used. The higher this number, the more requests an AI factory can handle concurrently. The horizontal axis represents the TPS for a single user, representing how long it takes for a model to give a user the first answer to a prompt. The higher the valu

Before building an AI factory, it’s important to understand the economics of inference — how to balance costs, energy efficiency and an increasing demand for AI. Throughput refers to the volume of tokens that a model can produce. Latency is the amount of tokens that the model can output in a specific amount of time, which is often measured in time to first token — how long it takes before the first output appears — and time per output token, or how fast each additional token comes out. Goodput is a newer metric, measuring how much useful output a system can deliver while hitting key latency ta

An AI factory transforms AI from a series of isolated experiments into a scalable, repeatable and reliable engine for innovation and business value. NVIDIA provides all the components needed to build AI factories, including accelerated computing, high-performance GPUs, high-bandwidth networking and optimized software. NVIDIA Blackwell GPUs, for example, can be connected via networking, liquid-cooled for energy efficiency and orchestrated with AI software. The NVIDIA Dynamo open-source inference platform offers an operating system for AI factories. It’s built to accelerate and scale AI with max

Measuring the impact of AI agents isn’t just a box to check — it’s essential to maximizing investment. The way users define success will directly shape how well these systems deliver value. Too often, businesses deploy agents without a clear measurement framework, making it difficult to prove return on investment or identify areas for improvement. When setting up an evaluation strategy, users should consider which metrics matter most for their goals. For example: Adoption and engagement: Track whether the technology is being embraced. Metrics include how many eligible users interact with the a