MegaETH's Testnet Achieves 1700 MGAS/s, Surpassing Competitors by 1600%

MegaETH has achieved a significant milestone in the blockchain industry by reaching 1700 MGAS/s in computational throughput on its testnet as of March 31, 2025. This performance surpasses other EVM chains, including Monad and opBNB, by a considerable margin. Monad achieved 300 MGAS/s, while opBNB reached 100 MGAS/s. In comparison, Ethereum L1 managed only 1.25 MGAS/s. MGAS/s measures the number of computational gas units processed per second, indicating MegaETH's superior raw processing power.

Adding to the momentum, MegaETH has secured a $140 million funding round led by Andreessen Horowitz (a16z). This investment underscores institutional trust in MegaETH's potential to address the scalability trilemma while maintaining compatibility with the Ethereum Virtual Machine (EVM). The funds will be used to expand development and security infrastructure, launch on the mainnet with ambitious throughput targets, and attract decentralized finance (DeFi) and gaming decentralized applications (dApps) that require high computational efficiency.

MegaETH's breakthrough is timely, as decentralized physical infrastructure (DePIN), decentralized storage, and on-chain AI/GPU workloads are gaining traction. These advancements necessitate blockchains capable of handling heavy, high-frequency computations. MegaETH is positioning itself as a base layer for next-generation use cases, including real-time AI inference on-chain, massive multiplayer crypto gaming, sensor-heavy DePIN applications, and high-volume DeFi protocols.

While still in the testnet phase, MegaETH's early performance metrics are impressive. If it can deliver similar results on the mainnet and maintain EVM compatibility, it could become the preferred chain for developers seeking to move away from slower Layer 1 (L1) solutions. With a16z's backing and growing interest in high-performance smart contract platforms, MegaETH is poised to lead the next wave of EVM development, especially as Ethereum L1 faces scaling challenges.