Efficiency Wins the Inference Era explains why built-in governance matters
Titled Efficiency Wins the Inference Era, the whitepaper focuses on the changing economics of AI inference, the process through which a trained model generates a response or completes a task each time it is used.
As generative AI becomes embedded in daily business operations, each interaction consumes tokens, the units a model processes to interpret prompts and generate outputs. Each interaction therefore carries a compute cost.
Assessing tokens
The whitepaper’s central argument is that the quoted price of an individual token provides an incomplete view of the real cost of production AI. Instead, organizations should assess tokens per second per dollar: how much useful output a system delivers, what it costs to generate, and how quickly it reaches the user.
This shifts the focus from simply purchasing lower-cost tokens to determining whether AI can deliver useful business outcomes repeatedly, economically, and at the speed each use case requires.
Workload
Achieving this requires organizations to move away from running every workload on the same model and infrastructure. Different tasks have different requirements, from real-time applications that depend on low latency to high-volume workloads that benefit from greater throughput.
Core42 Compass is designed around this principle, routing each workload to the most appropriate combination of model, accelerator, and deployment path across diverse silicon, including NVIDIA, AMD, Qualcomm, and Cerebras.
The platform operates with a 99.5% availability commitment, serves more than seven million API requests and processes well over 100 billion tokens per week across production workloads. Its fastest Cerebras inference path reports throughput improvements of up to twenty times.
