Nexus Layer1 Testnet Achieves Significant Progress: Can the "Planetary-Scale Supercomputer" with a Claimed Thousand-Fold zkVM Performance Boost Drive a Distributed Computing Revolution in the AI Era?

At a critical juncture in the integrated development of blockchain and artificial intelligence, Nexus Layer1 has emerged as a new generation infrastructure project.

The project innovatively designs the blockchain protocol as a "Planetary-scale Blockchain Supercomputer," and through its zero-knowledge virtual machine (zkVM) core technology, it is committed to building the infrastructure for a "verifiable internet."

Its technical architecture exhibits three distinct characteristics:

First, by integrating global distributed computing resources, it provides verifiable computing services for data-intensive scenarios such as AI training and inference.

Second, it adopts an innovative "universal proof" system to transform complex blockchain state verification into efficient and verifiable mathematical proofs.

Finally, it emphasizes the network's universal accessibility, significantly lowering the barrier to participation.

With a series of technological breakthroughs achieved since March 2025, especially the successful deployment of Testnet III on June 24, the project has officially entered the final sprint before the mainnet launch.

In early March 2025, the core computing engine zkVM underwent a major upgrade to version 3.0.

It is worth noting that this upgrade is not a simple performance optimization, but a comprehensive innovation from the underlying architecture to the development tools.

According to technical details disclosed by Nexus founder Daniel Marin on his X platform account, the new version achieves an approximately 1000-fold performance increase through innovations such as improved proof system algorithms, optimized compiler chains, and restructured memory models.

At the same time, the accompanying 130-page formal specification document attempts to provide developers with more rigorous technical references.

It is worth noting that the project team also simultaneously released a detailed end-to-end benchmark test report covering various scenarios from simple smart contracts to complex AI model inference.

Actual deployment results show that the upgraded platform can support larger-scale and more complex distributed computing tasks, especially demonstrating unique advantages in scenarios such as parallel AI model training.

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