The AI Data Center Economy Runs on Tokens per Second per Watt
In early 2026, Microsoft disclosed an $80 billion unfulfilled Azure order backlog due to insufficient electricity to power the deployments. CEO Satya Nadella confirmed on the record that their GPUs are sitting idle in inventory, waiting for installations that can’t proceed until the grid can deliver the load.
What Becomes Possible With 100x More Tokens?
What if we could generate 100x more tokens for the same budget? What new possibilities and world-changing applications would open up if the cost of AI stopped being the deciding factor?
Why AI Inference Could Be the Next Global Energy Crisis
The AI revolution has officially entered the stage of massive-scale deployment. But as organizations scale AI-powered applications to serve billions of daily queries, they are quickly finding that energy consumption is threatening their efforts.
The Inference Economy: LLM Inference Is Everywhere, Not Just in Your Chatbot
When most people think of Large Language Models (LLMs), they think of chatbots. However, this perspective drastically understates the true scope and technical necessity of the inference market. Inference is much more than typing a prompt and receiving a response. In reality, it’s become a basic compute primitive that powers the entire AI lifecycle.
How ElastixAI Delivers the Lowest Cost per Token in LLM Inference
ElastixAI is reshaping inference infrastructure with a software-defined hardware approach that slashes CapEx, reduces power consumption by up to 80%, and delivers unmatched flexibility for next-generation optimizations.
Five Reasons Why FPGAs Hit the Sweet Spot for LLM Inference
As LLMs evolve weekly, fixed-function GPUs struggle to keep up. This article explores why FPGAs offer the perfect balance between efficiency and adaptability—unlocking lower cost per token, eliminating dark silicon waste, and enabling native support for next-generation ML optimizations.