After reading the latest white paper released by Reddio, it indeed integrates automated AI execution into the grand narrative of EVM, effectively filling the gap in the entire Ethereum ecosystem in the AI track. It makes a lot of sense. So, why can parallel EVM seamlessly connect with AI? What are the underlying logic and technical principles? Here’s my understanding:

1) The narrative of "parallel EVM" has always been characterized as a key battle to bridge the gap between the old and outdated EVM ecosystem and high-performance chain technologies like Solana and Sui. Therefore, the previous market hype around Sei and Monad's $225 million massive financing have pushed parallel EVM to unprecedented heights.

In contrast, Reddio, also a parallel EVM public chain led by Paradigm, seems to be much more low-key, without market expectations like financing, ICOs, or KOL rounds. It has simply been showcasing its testnet's stable thousands of TPS data. Recently, the official announcement of a snapshot clearly indicates an intention to take the stage first and validate the ecological value of parallel EVM within the Ethereum ecosystem.

2) So, why is parallel EVM an effective supplement to the technical capability bottlenecks of the Ethereum ecosystem? Simply put, the original single-threaded execution + serial transaction order execution of EVM is a fundamental limitation. Parallel EVM leverages the parallel computing capabilities of modern hardware (CPU, GPU), along with some I/O asynchronous storage processing, state access optimization, etc., to achieve simultaneous execution of large-scale batch transactions.

The technical implementation logic revealed in the Reddio white paper is roughly as follows: Reddio has an execution network composed of GPU nodes and uses a CUDA "code translator" to convert general EVM opcodes into complex, computation-intensive tasks that can be executed on GPUs. Additionally, with other I/O asynchronous storage optimizations, state access management optimizations, optimistic concurrency control, etc., it achieves the capability of parallel transaction processing.

3) Since parallel EVM essentially leverages the performance advantages of "hardware," AI application scenarios naturally require large-scale parallel computing and intensive computation processing. A powerful hardware setup can simultaneously serve both parallel EVM and AI application scenarios. Thus, another layer of narrative imagination for parallel EVM + AI has been opened. Parallel EVM chains can deploy large AI models on-chain and allow smart contracts to directly control and schedule AI, while also applying ZK, TEE, and other data privacy and verifiability capabilities, achieving a native integration of blockchain and AI. For example, real-time AI inference, AI Oracles, off-chain AI trading strategy optimization, etc.

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