The Hidden Cost of EVM's Sequential Processing

Sequential execution is the bottleneck. The EVM processes transactions one after another, creating a single queue for all computation. This design prevents parallel processing of independent transactions, capping throughput and inflating costs for every user.

The cost is systemic latency. This serialization forces protocols like Uniswap and Aave to compete for the same linear block space. High-frequency activities, such as arbitrage between Curve pools, become economically unviable for all but the most optimized searchers.

Evidence is in the gas. During network congestion, over 90% of a transaction's cost is the base fee for this sequential queue, not the actual computation. Layer 2s like Arbitrum and Optimism inherit this core constraint, merely shifting the bottleneck.

Sequential execution is a tax. Every transaction on Ethereum and its L2s like Arbitrum and Optimism must wait its turn for a single CPU thread, creating artificial congestion and inflating gas costs even when network resources are idle.

Parallelism eliminates state contention. Protocols like Solana and Sui execute independent transactions simultaneously. This means a Uniswap swap and an NFT mint don't block each other, directly translating to lower latency and higher theoretical throughput.

The EVM's global state is the bottleneck. Every transaction must serially read and write to a single shared state tree. This creates state contention that parallel hardware cannot solve, capping scalability at the protocol design level.

Evidence: During peak demand, over 90% of Arbitrum's gas is spent on L1 data posting, but its execution layer remains bottlenecked by EVM sequencing. True scaling requires re-architecting the execution environment itself.

Single-threaded execution is the EVM's core architectural constraint. Every transaction, from a simple transfer to a complex Uniswap swap, must be processed in a strict, global sequence. This deterministic ordering prevents race conditions but creates a physical ceiling on network capacity.

The gas auction model directly results from this bottleneck. With only one operation slot per block, users must outbid each other for priority. This turns transaction ordering into a pure financial auction, decoupling fees from computational cost and creating volatile, unpredictable pricing.

Parallelizable operations are blocked. A Uniswap swap on Ethereum and a simple NFT mint on OpenSea have no data dependencies, yet they cannot execute concurrently. Modern chains like Solana and Sui achieve orders-of-magnitude higher throughput by allowing this parallelism.

Cross-chain infrastructure suffers. Protocols like LayerZero and Across must serialize their verification and execution steps on the EVM. This sequential processing adds latency and cost to every cross-chain message, making native interoperability inherently expensive.

Sequential execution is the root constraint. Modular designs like Celestia or EigenDA solve data availability and consensus, but the execution layer remains a single-threaded EVM. This creates a hard cap on state transitions per second, regardless of how fast data is posted.

Parallel EVMs are a band-aid. Projects like Monad and Sei attempt to parallelize transaction processing, but they break composability. A DeFi transaction interacting with Uniswap and Aave cannot be parallelized if the second action depends on the first's outcome.

The cost is hidden latency. Modular stacks add settlement and proving delays. A user's cross-rollup swap via Across or a LayerZero message must wait for sequential execution on both the source and destination chains, multiplying finality time.

Evidence: The Solana Virtual Machine (SVM) demonstrates that a single-threaded runtime, even with aggressive parallelization, hits a throughput wall. Its theoretical limit, before network bottlenecks, is defined by the speed of a single CPU core updating a global state.