The Gradient 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, consumer-grade devices. This is inference, recomposed.

🔄 Echo: The Distributed Reinforcement Learning Framework

As AI systems grow in scale and generality, RL has become a critical layer for alignment. Techniques like RLHF (RL from Human Feedback) and DPO (Direct Preference Optimization) are increasingly central to ensuring that model behavior aligns with human goals and values.

Echo introduces a clean architectural separation. Rather than co-locating all RL workloads on a single cluster, it distributes inference and training across two dedicated, heterogeneous swarms. Each swarm is optimized for its specific computational profile and can scale independently.

  • Inference Swarm–Optimized for low-latency trajectory generation. Echo leverages the Inference Swarm from Parallax—Gradient’s distributed inference engine. Running across globally distributed, heterogeneous consumer-grade hardware, including RTX 40/50-series GPUs and Apple Silicon (M-series), it enables scalable rollout generation and supports a wide range of AI workloads on everyday devices.

  • Training Swarm–Optimized for high-throughput gradient updates. It operates on datacenter-grade accelerators such as A100 and H100 clusters, ensuring efficient and stable policy optimization at scale.

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