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InferenceMAX v1, a new benchmark from SemiAnalysis released Monday, is the latest to highlight Blackwell’s inference leadership. It runs popular models across leading platforms, measures performance for a wide range of use cases and publishes results anyone can verify. Why do benchmarks like this matter? Because modern AI isn’t just about raw speed — it’s about efficiency and economics at scale. As models shift from one-shot replies to multistep reasoning and tool use, they generate far more tokens per query, dramatically increasing compute demands. NVIDIA’s open-source collaborations with Ope

AI is moving from pilots to AI factories — infrastructure that manufactures intelligence by turning data into tokens and decisions in real time. Open, frequently updated benchmarks help teams make informed platform choices, tune for cost per token, latency service-level agreements and utilization across changing workloads. Learn more about how to calculate lowest cost per token and how the NVIDIA Think SMART framework drives cost efficient inference.

Understanding how to optimize token cost requires looking at the equation for calculating cost per million tokens. In this equation, many enterprises evaluating AI infrastructure focus on the numerator: the cost per GPU per hour. For cloud deployments, this is the hourly rate paid to a cloud provider; for on-premises deployments, it’s the effective hourly cost derived from amortizing owned infrastructure. The real key to reducing token cost, however, lies in the denominator: maximizing the delivered token output. That denominator carries two business implications. Minimize token cost: When thi

Metrics like tokens per watt, cost per million tokens and TPS/user matter as much as throughput. In fact, for power-limited AI factories, Blackwell delivers 10x throughput per megawatt for mixture-of-experts models compared with the previous generation, which translates into higher token revenue. The cost per token is crucial for evaluating AI model efficiency, directly impacting operational expenses. The NVIDIA Blackwell architecture lowered cost per million tokens by 15x versus the previous generation, leading to substantial savings and fostering wider AI deployment and innovation.