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ElastixAI

Insights.

Research, perspectives, and updates from the ElastixAI team.

An ElastixAI title card reading “From Telephones to Transformers: A Brief History of Quantization”.

From Telephones to Transformers: A Brief History of Quantization

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.

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A green mountain diagram of the ElastixAI opportunity zone above the economic feasibility line.

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.

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A green pyramid chart showing 100X more tokens above a GPU-based cost barrier.

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?

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An iceberg with a small tip above the waterline and a vast mass below it.

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.

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High-voltage transmission towers silhouetted against an orange sunset.

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.

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A close-up of a circuit board with a brain motif etched onto the processor die.

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.

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An ElastixAI server chassis in a data center rack.

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.

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