Traditional data centers only stored, retrieved and processed data. In the generative and agentic AI era, these facilities have evolved into AI token factories. With AI inference becoming their primary workload, their primary output is intelligence manufactured in the form of tokens.
This transformation demands a corresponding shift in how the economics of AI infrastructure, including total cost of ownership (TCO), is assessed. Enterprises evaluating AI infrastructure still too often focus on peak chip specifications, compute cost or floating point operations per second for every dollar spent, aka FLOPS per dollar.
The distinction that matters is this:
The first two are merely input metrics. Optimizing for inputs while the business runs on output is a fundamental mismatch.
Cost per token determines whether enterprises can profitably scale AI. It’s the one TCO metric that directly accounts for hardware performance, software optimization, ecosystem support and real-world utilization — and NVIDIA delivers the lowest cost per token in the industry.
Understanding how to optimize token cost requires looking at the equation for calculating cost per million tokens.
![An equation describing how to calculate cost per million tokens. Cost per million tokens = [cost per GPU per hour / (tokens per GPU per second x 60 seconds x 60 minutes) ] x 1 million.](https://blogs.nvidia.com/wp-content/uploads/2026/04/inference-equation-token-5115300-scaled.png)
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.
So focusing only on the numerator means missing what drives the denominator. Think of it as an “inference iceberg”: The numerator sits above the surface, visible and easy to compare. The denominator is everything beneath the surface, which represents key factors that determine real-world token output. Accurately evaluating AI infrastructure starts with asking what lies beneath.
Every one of these algorithmic, hardware and software optimizations must be active and integrated, or the denominator collapses. A “cheaper” GPU that delivers significantly fewer tokens per second results in a much higher cost per token. AI infrastructure that gets it right across the full stack ensures that every optimization enhances the others.
The following data for the DeepSeek-R1 AI model demonstrates the difference between theoretical and actual business outcomes.
Looking at compute cost alone, the NVIDIA Blackwell platform appears to cost roughly 2x more than NVIDIA Hopper — but compute cost says nothing about the output that investment buys. An analysis of mere FLOPS per dollar suggests a 2x NVIDIA Blackwell advantage compared with the NVIDIA Hopper architecture. However, the actual outcome is orders of magnitude different: Blackwell delivers more than 50x greater token output per watt than Hopper, resulting in nearly 35x lower cost per million tokens.
| Metric | NVIDIA Hopper (HGX H200) | NVIDIA Blackwell (GB300 NVL72) | NVIDIA Blackwell Relative to Hopper |
|---|---|---|---|
| Cost per GPU per Hour ($) | $1.41 | $2.65 | 2x |
| FLOP per Dollar (PFLOPS) | 2.8 | 5.6 | 2x |
| Token Output per GPU | 90 | 6K | 65x |
| Token Output per MW | 54K | 2.8M | 50x |
| Cost per Million Tokens ($) | $4.20 | $0.12 | 35x lower |
Note: Data is sourced from NVIDIA analysis and the SemiAnalysis InferenceX v2 benchmark.
This massive divergence proves NVIDIA Blackwell delivers a massive leap in business value over the earlier Hopper generation that far outpaces any increase in system cost.
Comparing AI infrastructure based on compute cost or theoretical FLOPS per dollar isn’t just insufficient; it doesn’t provide an accurate representation of inference economics. As the data demonstrates, an accurate evaluation of AI infrastructure’s revenue potential and profitability requires a shift from input metrics to cost per token and delivered token output.
NVIDIA delivers the industry’s lowest token cost and highest token throughput through extreme codesign across compute, networking, memory, storage, software and partner technologies. Moreover, the constant optimization of open source inference software such as vLLM, SGLang, NVIDIA TensorRT-LLM and NVIDIA Dynamo built on the NVIDIA platform means that on existing NVIDIA infrastructure, token output continues to increase and the cost per token continues to decline long after it’s acquired.
Leading cloud providers and NVIDIA cloud partners are already delivering this advantage at scale. Partners such as CoreWeave, Nebius, Nscale and Together AI have deployed NVIDIA Blackwell infrastructure and optimized their stacks to bring enterprises the lowest token cost available today, with the full benefit of NVIDIA’s hardware, software and ecosystem codesign behind every interaction served.
