Research, perspectives, and updates from the ElastixAI team.
Model quality used to be AI’s limiting factor. Now, it’s model economics. Where cost per token, watts per rack, and tokens per second dominate the conversation, no topic is more pertinent than model optimizations. And, in that conversation, few optimizations are as well-known, and as misunderstood, as quantization.
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 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?
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.
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.
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.
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.
Research, product updates, and a closer look at efficient AI inference.