Rethinking the Inference Layer: How PAI3 Makes XLLMs Actually Work

We’re entering the age of XLLMs (extra-large, multimodal language models) capable of reasoning across text, vision, audio, and even code in a single pass.

These are the beginnings of true general-purpose AI systems. But as models grow more powerful, they’re also becoming more expensive to train and to run.

The Cost of Intelligence

Much of the conversation around large models centers on training costs, and for good reason. Training a frontier model can cost tens of millions of dollars and require access to elite GPU clusters.

But here’s the thing, inference costs, the cost of actually using the model once it’s trained are ballooning even faster.

Enterprise AI teams report that inference now consumes up to 70% of total AI compute spend. This is because these models are increasingly being used in real-time, across millions of small queries.

Every chatbot conversation, every AI agent interaction, every auto-generated summary, each one runs a slice of a massive model. And that model has to live somewhere.

Centralized AI Infrastructure Can’t Keep Up

Today, most AI inference runs on centralized infrastructure such as AWS, Azure, Google Cloud, or a few well-funded labs with their own GPU stacks. It’s expensive, energy-intensive, and geographically limited.

Worse, it’s fragile. A single constraint or pricing change can impact thousands of downstream apps and developers.

That’s a risky model in a world moving toward AI agents, ambient interfaces, and real-time multimodal reasoning. As inference becomes more fragmented and frequent, we need a new kind of infrastructure, one that can scale across the globe, cheaply and permissionlessly.

Rethinking the Inference Layer

PAI3 is building a decentralized network of AI nodes, each one capable of running containerized inference tasks, from language models to agents to multimodal workflows. Think of it as the distributed backend for the next generation of AI applications.

Here’s what makes it different:

• Distributed Inference: Instead of relying on centralized servers, tasks are routed across thousands of user-operated nodes.

• Containerized AI: Each node runs lightweight, secure containers that can host different models on demand.

• Permissionless Monetization: Node operators earn tokens for every inference job they complete creating an incentive-aligned, self-sustaining ecosystem.

The Importance

XLLMs are only going to get larger, more capable, and more demanding. We are hurtling toward an era where every app, device, and browser tab could be powered by live AI agents.

But none of that is sustainable unless we solve the inference clog.

PAI3 offers a fresh approach, scaling up using decentralized compute networks to meet global demand. It’s more efficient, more resilient, and ultimately more equitable.

This mirrors what we saw with the rise of CDNs and edge computing in Web2. As demand for real-time content delivery exploded, the internet built smarter distribution networks. PAI3 is doing the same for AI.

We’re all excited about what XLLMs can do, and how big they can get through a decentralized, user-owned infrastructure that turns AI inference into something we can all host, power, and earn from.