Injective Unveils Native EVM Support for Enhanced Blockchain Performance and Interoperability

Injective, a decentralized layer-1 blockchain platform, set to integrate native Ethereum Virtual Machine (EVM) support into its architecture. This integration aims to boost performance and interoperability of the Injective network.

Here are some of its features:

A Fully Integrated EVM for Seamless Experience

Injective’s native EVM implementation is embedded directly into the core of the blockchain, rather than being an add-on or modular tool. This ensures that EVM operates on Injective’s dedicated infrastructure, creating a unified and cohesive execution layer. 

Unlike solutions that rely on third-party bridges or external modules, Injective's approach reportedly eliminates any dependencies on external systems, making the network faster, more secure, and more reliable. Per reports, this fully integrated approach enables smooth interoperability with Ethereum’s ecosystem and enhances its capability to serve as a hub for various technologies and decentralized applications (dApps).

Bridging the Gap Between Ethereum and DeFi

The introduction of the EVM is a part of Injective's larger Multi-VM initiative, which seeks to make the platform compatible with multiple virtual machines, including both EVM and WebAssembly (WASM). 

This opens up the Injective blockchain to a broader community of developers, especially those who are familiar with Ethereum’s development environment. It also positions Injective as a bridge between traditional finance and decentralized finance, supporting innovations that could further evolve both sectors.

The network's infrastructure ensures full compatibility with Ethereum's latest tools, such as the most up-to-date version of Geth. As a result, developers are able to leverage the latest Solidity features and Ethereum standards as they transition from Ethereum.

Optimizing for the Future: EVM for AI-Driven Finance

Injective is also pioneering new avenues by integrating on-chain AI inference models, which support decentralized artificial intelligence (DeFAI). This integration allows for AI-driven financial applications, offering a new layer of possibilities for intelligent data indexing, agent-based infrastructure, and collective intelligence. 

A Leap Beyond Current Standards

Injective has tested its native EVM to ensure the effectiveness of the execution layer. Early reportedly testing revealed that Injective’s native EVM could handle transactions up to eight times faster than other leading blockchain networks. 

Tests showed that the network is capable of processing 9,000 lightweight transactions per second under controlled conditions, and up to 800 Ethereum-based transactions per second in real-world settings. 

Furthermore, using bundled transaction testing, Injective demonstrated a throughput of 12,500 transactions per second, with plans to scale this number even higher as the platform evolves.

Also, according to the team, in its mainnet simulation, Injective’s EVM surpassed Ethereum's typical performance by over 400%, making it one of the fastest blockchain networks for executing Ethereum-compatible transactions. With real-time handling of 1 million lightweight EVM transactions, Injective reportedly maintains stable block times and minimal transaction delays, paving the way for its scalability in real-world use cases.

Injective also leveraged its new account abstraction mechanisms to push performance further. This involves optimizing bundled transactions and enabling more efficient processing of multiple user operations within a single transaction. These optimizations could eventually push Injective’s theoretical throughput beyond 20,000 TPS.

Injective is launching a private developer network (devnet) for initial testing, with the mainnet release slated for later this year.

The recent update comes a few months after Injective launched iAgent, a software development kit (SDK) that enables users to create their own on-chain artificial intelligence agents. 

iAgent integrates OpenAI's ChatGPT and other large language models, allowing users to command AI agents to perform tasks within the Injective ecosystem. Users can build multiple, independent agents to tackle specific goals, such as one that tracks market data while another executes trades, according to Injective.