The Base Stack

The Base Stack for Decentralized AI

At Gradient Network, we believe that decentralized intelligence requires a few foundational primitives: compute, communication, and orchestration. Together, they form the backbone of a new machine internet—one that is open, sovereign, and powered by millions. To bring this vision to life, we’ve set the stack in motion with two foundational building blocks—Lattica and Parallax.

🌍 Lattica: The Universal Data Motion Engine

A robust, efficient peer-to-peer connectivity layer is essential for enabling decentralized compute of any kind. Over the past few months, in pursuit of a global peer-to-peer content delivery network (CDN), we unintentionally conducted one of the largest real-world experiments in internet connectivity mapping as the peer-to-peer CDN approaches commercialization. This was made possible by millions of participants across our Sentry Node network, showcasing the vast potential of decentralized systems powered by community engagement.

Today, that vision has evolved into Lattica—our universal peer-to-peer data communication protocol and the connectivity backbone of the decentralized AI stack.

▶️Parallax: The World Inference Engine

As agentic AI applications proliferate, the demand for scalable inference infrastructure is accelerating, alongside rising needs for data sovereignty, reliability, and cost efficiency. Parallax is our response: a decentralized inference protocol purpose-built for this new paradigm.

What sets Parallax apart is its ability to go far beyond running small models on local endpoints. It enables large foundation models to be decomposed, distributed, and collaboratively executed across a global mesh of heterogeneous devices. This is inference, recomposed.

To support this, Parallax is designed with a set of critical capabilities:

• Scalability: Harnesses a global mesh of compute nodes—across diverse device classes—to scale inference beyond centralized limits.

• Modular Execution: Runs large models as orchestrated segments across distributed nodes, enabling flexible, fault-tolerant deployment.

• Privacy-Preservation: Ensures user data remains secure and confidential, honoring one of the core motivations to decentralize inference in the first place.

• Verifiability: Enables transparent validation of inference outputs, reinforcing trust in decentralized systems.

• Reliability: Delivers fault-tolerant, consistently performant inference, even in highly distributed and heterogeneous environments.

Lattica and Parallax are just the beginning. More groundbreaking protocols are on the horizon, each reinforcing our commitment to a truly decentralized AI runtime.