Parallel EVM: Monad and Sei's Path to High Performance

A parallel EVM is a high-performance blockchain technology that executes non-conflicting Ethereum transactions at the same time while preserving a deterministic state result.

A traditional EVM usually processes transactions one by one in block order. Even when two transactions access entirely different accounts and contracts, they still wait in the same queue. A parallel EVM attempts to use multicore CPUs to run independent transactions concurrently, detect state conflicts, roll back invalid results, and commit transactions in the canonical order.

Monad and Sei are representative projects pursuing this approach. Both aim to retain Solidity, EVM bytecode, and familiar Ethereum tooling while redesigning the underlying systems for execution scheduling, state databases, consensus, and block propagation. Performance, however, depends on more than parallelism. Popular contracts, storage access, node hardware, network propagation, and decentralization also shape the real user experience.

To place parallel EVMs alongside AI, DePIN, modular blockchains, interoperability, zero-knowledge proofs, and other emerging sectors, see The 2025 Web3 Frontier Landscape.

What Is a Parallel EVM?

A parallel EVM is not a new smart contract language or a simple increase to the block gas limit. It changes how transactions execute inside a node: a scheduler assigns transactions in a block to multiple execution threads, records the state each transaction reads and writes, and then ensures that the final result matches the prescribed serial order.

Determinism is a fundamental EVM requirement. Given the same block, initial state, and protocol rules, every honest node must derive the same final state root. Parallel execution cannot produce different results because of thread timing, CPU models, or network latency; otherwise, nodes would fork.

The essence of a parallel EVM is therefore not merely “starting at the same time,” but “committing deterministically after concurrent computation.” The system must manage dependencies among account balances, nonces, contract storage, event logs, and cross-contract calls, discarding or recomputing work when it detects a conflict.

Why Does the Traditional EVM Usually Execute Sequentially?

Ethereum transactions have an explicit order within each block. A later transaction may read a balance, price, allowance, or liquidity state just written by an earlier one. Processing transactions one at a time in order is the simplest way to guarantee consistent results and lets developers reason about causality between transactions.

The limitation of sequential execution is that it cannot fully use modern multicore hardware. A block might contain an NFT mint, transfers between independent accounts, and interactions with unrelated DApps. Although these operations may share no state, they still wait in a single execution thread.

Adding CPU cores alone does not automatically accelerate the EVM. A client must identify dependencies, schedule work, preserve temporary states, detect conflicts, and commit results canonically. The state database must also support many concurrent reads; otherwise, execution threads will ultimately wait on disk and memory access.

How Do Transaction Conflicts Arise?

3.1 What Are Read and Write Sets?

When a transaction executes, it reads and modifies a set of state keys. A transfer, for example, reads the sender's balance and nonce and then writes both parties' balances. A DEX trade may read pool reserves, user allowances, and router contract state. These keys form the transaction's read set and write set.

Transactions that access entirely different keys can generally run in parallel. A read-write conflict exists if one transaction writes a key that another reads, while a write-write conflict exists if both write the same key. Changing the commit order could then produce a different result.

3.2 Why Does Hot State Limit Parallelism?

Many users may simultaneously trade the same popular token, mint the same NFT, or call the same liquidity pool. Even when their addresses differ, a shared contract's total supply, reserves, global counters, or price state can become hot spots.

The more concentrated these hot spots are, the fewer transactions can execute safely in parallel and the more rollbacks and re-executions occur. Parallel EVMs can significantly improve workloads with many independent account operations, but they cannot make every on-chain activity scale linearly with the number of CPU cores.

How Can a Parallel EVM Schedule Transactions?

4.1 Declaring State Access in Advance

One approach requires a transaction or contract to declare which accounts and states it will access. Before execution, the scheduler builds a dependency graph and runs non-overlapping groups in parallel. Conflicts become more predictable, but developers and users must provide accurate access lists, and dynamic contract calls can be difficult to enumerate in advance.

4.2 Static Dependency Analysis

A client can also analyze bytecode and transaction inputs to predict which state may be accessed. However, EVM contracts support dynamic calls, computed storage keys, and runtime branches, so static analysis often has to be conservative. An estimate that is too broad misses parallelization opportunities, while one that is too narrow requires additional conflict handling.

4.3 Optimistic Parallel Execution

An optimistic system initially assumes transactions do not conflict, executes them concurrently, and records their actual read and write sets. During the commit phase, it validates read versions in the order specified by the block. If a transaction read stale state that an earlier transaction subsequently modified, its result is discarded and the transaction runs again.

This approach does not require Solidity developers to annotate every dependency manually and is therefore friendlier to existing contracts. However, workloads with many conflicts waste computation, while schedulers, versioned state, and rollback logic become more complex.

How Does Monad Build a High-Performance EVM?

Monad aims to redesign execution, consensus, and state storage while retaining compatibility with EVM bytecode and Ethereum RPCs. In addition to parallel execution, it attempts to pipeline multiple system stages so the CPU, disk, and network spend less time waiting for one another.

5.1 Optimistic Parallel Execution

Monad nodes can execute multiple transactions concurrently while tracking their inputs and outputs. Final commits still follow the canonical order in the block. If an earlier transaction changes state that a later transaction has already read, the later result becomes invalid and is recomputed.

Developers can continue writing contracts according to the EVM's sequential semantics. The client handles most parallelization, so applications do not all need to adopt a new programming model. Compatibility does not mean every edge case behaves identically, however. Projects should still test precompiles, gas behavior, RPCs, tracing, and infrastructure support before deployment.

5.2 Deferred Execution and Pipelining

Monad's architecture pipelines consensus ordering and transaction execution. The network can first agree on transaction order and complete execution in a later stage, instead of making each consensus round wait for full execution to finish. This improves resource utilization but also requires the protocol to define how it handles execution results, state commitments, and invalid transactions.

Deferred execution does not allow invalid state to pass permanently. Nodes must still execute the agreed transaction sequence independently and remain consistent; ordering and computation merely overlap in time. When interpreting finality, users should distinguish among a transaction being ordered, its execution completing, and its state becoming safe to rely on.

5.3 MonadBFT

MonadBFT is a Byzantine fault-tolerant consensus design for pipelined block production. Consensus enables validators to agree on transaction order and blocks, while pipelining reduces idle time between consecutive blocks.

High-performance consensus still depends on validator count, geographic distribution, network latency, bandwidth, and failure recovery. Low-latency results in a laboratory environment do not directly represent how globally distributed nodes perform during congestion or partial outages.

5.4 MonadDB

MonadDB is a database designed around blockchain state access. Parallel execution generates many random reads, temporary versions, and asynchronous writes, and general-purpose databases may not make full use of SSDs and multiple threads under this workload.

A specialized state database seeks to reduce disk waits, let execution threads request data concurrently, and maintain Merkle state commitments efficiently. Database speed, cache hit rates, state growth, and node recovery jointly determine long-term performance; single-transaction computation speed tells only part of the story.

How Does Sei Implement a Parallel EVM?

Sei was initially built for high-performance trading applications and later introduced an EVM execution environment with optimistic parallelization. Its approach also extends beyond execution threads, using SeiDB, consensus, and block-processing optimizations to reduce state-access costs and waiting time.

6.1 Optimistic Parallelization

Sei's parallel execution mechanism first runs transactions concurrently and then detects dependencies from their actual state access. Results can commit in parallel when transactions do not conflict. When conflicts occur, the system re-executes the affected transactions according to deterministic rules.

This reduces the burden on applications to declare access keys explicitly and can accommodate ordinary Solidity contracts. The real benefit depends on the proportion of independent transactions in a block. If many transactions contend for the same contract state, concurrently produced intermediate results may frequently become invalid.

6.2 SeiDB

SeiDB is optimized for high-throughput state reads and writes. A blockchain database must preserve current state, calculate state commitments, support historical queries, and synchronize nodes. If every update waits for an expensive disk operation, adding execution threads cannot improve overall throughput.

By reorganizing state storage and commitment workflows, SeiDB aims to reduce write amplification and improve synchronization and node efficiency. A sound evaluation should examine long-term state growth, snapshot recovery, archival requirements, and the cost of ordinary hardware rather than only short-term benchmarks.

6.3 Twin-Turbo Consensus

Sei's Twin-Turbo Consensus combines intelligent block propagation with optimistic block processing. Validators may receive transaction information in advance, allowing the proposer to send more compact block references. Nodes can also start processing as soon as they receive a proposal, reducing propagation and execution wait times.

This optimization assumes most nodes have already seen the relevant transactions and must safely handle missing transactions and invalid proposals. When network quality deteriorates, the protocol must fall back to retrieving complete data and performing normal validation. Performance cannot be built on skipping verification.

What Do Monad and Sei Have in Common?

First, both emphasize EVM compatibility. Developers can keep using Solidity, common wallets, RPCs, and development tools, reducing the cost of migrating applications from Ethereum.

Second, both use optimistic parallelism. Their systems do not require every transaction to declare all state access accurately in advance; instead, they inspect actual conflicts after execution and recompute when necessary.

Third, both treat performance as a systems-engineering problem. Parallel execution is only one component; state databases, consensus, propagation, clients, and node hardware must be optimized together.

Fourth, both are constrained by workload. More independent transactions create more room for parallelism, while concentrated hot state produces more conflicts and re-execution. No fixed TPS figure can represent every application scenario.

Fifth, both must navigate trade-offs between compatibility and decentralization. Shorter block times and higher throughput may increase bandwidth, SSD, memory, and operational requirements for validators, so node participation thresholds require ongoing scrutiny.

How Do Monad and Sei Differ Technically?

Monad starts with components such as EVM execution, MonadDB, and MonadBFT and emphasizes redesigning the full node stack for a highly concurrent EVM. Its deferred execution pipelines consensus ordering with computation, highlighting vertically integrated optimization.

Sei builds on the evolution of its own chain architecture, combining the EVM, optimistic parallelization, SeiDB, and Twin-Turbo Consensus while retaining existing ecosystem components. Its path resembles an expansion from a high-performance application chain toward EVM developers.

The components of the two systems do not map directly one to one. A useful comparison examines equivalent layers: how parallel scheduling handles conflicts, how the state database reads and commits data, when consensus confirms ordering, when users obtain dependable finality, and what hardware nodes require.

Specific network parameters, client versions, governance permissions, and ecosystem support will continue to change. Before deployment, consult the latest official documentation and measurements from the live network.

How Does a Parallel EVM Relate to a Modular Blockchain?

A parallel EVM primarily optimizes the execution layer so a group of nodes can use multicore hardware more effectively. A modular blockchain distributes execution, settlement, consensus, and data availability among different systems. The two approaches address different problems and can be combined.

A parallel EVM chain can be a monolithic L1 whose validator set performs execution, consensus, and data availability. It can also serve as a Rollup execution environment that delegates data publication and settlement to other networks.

For more on this division of responsibilities, read Modular Blockchains: Celestia and the Separation of Execution and Settlement. Modularity reduces the responsibilities placed on an individual component, while parallelism improves efficiency inside the execution component. They are not substitutes.

How Does a Parallel EVM Differ From Layer 2?

A parallel L1 generally has its own validators order, execute, and reach consensus on high-throughput transactions directly. A Rollup moves execution to L2, submits transaction data, state commitments, or proofs to L1, and relies on L1 for settlement or data availability.

A parallel L1 may offer a more direct user experience, but its own validators and protocol provide security. A Rollup can inherit some security from its settlement layer, yet it must contend with sequencers, proofs, bridges, data publication, and withdrawal delays.

Both approaches can increase throughput and can incorporate parallel execution. When comparing scaling solutions, look beyond fees to finality, data location, verification costs, bridging, and failure exits. For additional context, see What Is Layer 2? A Complete Guide to Ethereum Scaling.

Which Applications Benefit Most From a Parallel EVM?

The first category is workloads with many independent user actions, such as social interactions, game quests, NFT operations, and simple transfers between different accounts. With less overlapping state, a scheduler can make better use of multiple cores.

The second category includes on-chain order books and trading applications. These systems need low latency and frequent updates, but they can still create hot spots if every order modifies the same global state. Applications can improve state layout by separating markets and accounts or by batching operations.

The third category is high-frequency consumer applications such as loyalty points, ticketing, prediction markets, and real-time interactions. EVM compatibility allows them to reuse mature tools, while parallel execution can handle more simultaneous requests.

The fourth category is a shared public chain hosting many applications. When users interact with separate contracts, the workload naturally contains more independent state, and parallel scheduling can reduce execution queues between unrelated applications.

A parallel EVM does not automatically optimize a single, extremely complex transaction. Extensive computation, cross-contract calls, and storage access inside one transaction generally remain limited by single-threaded logic, gas constraints, and database latency.

What Are the Risks and Limitations of Parallel EVMs?

12.1 High-Conflict Workloads Can Degrade Performance

A popular contract or state key can prevent most transactions from running in parallel. The system still has to roll back and re-execute work, and the additional scheduling overhead may even offset the benefits of concurrency.

12.2 Concurrency Bugs Are Harder to Diagnose

Clients must maintain state versions, read sets, rollback logic, and commit ordering correctly. An implementation error could make nodes derive different states, creating a consensus-level risk. Multi-client testing, fuzz testing, and extended mainnet operation are therefore important.

12.3 EVM Compatibility Is Not Complete Equivalence

Runnable bytecode does not guarantee identical behavior across every RPC, debugger, indexer, precompile, gas estimator, and transaction tracer. Complex applications should undergo end-to-end testing before migration.

12.4 Node Hardware Requirements May Increase

Higher throughput creates more network data, state access, and historical storage. Even if parallelism lowers per-transaction execution time, validators may still need more powerful CPUs, memory, SSDs, and bandwidth, which can affect the distribution of node participation.

12.5 High TPS Does Not Guarantee Low Latency

Throughput measures how many transactions a system processes per unit of time, while latency measures how long one transaction takes to confirm. Larger batches can raise TPS while increasing queue times, and a fast preconfirmation is not necessarily irreversible finality.

12.6 Benchmark Conditions May Not Be Comparable

Simple transfers, complex DeFi, independent state, and hot state produce very different results. Node count, hardware, block gas, failed transactions, and data propagation also affect reported figures. Evaluations should look for public testing methods and sustained live-network data.

12.7 Consensus and Execution Can Still Be Centralized

A parallel EVM improves computation within a node; it does not automatically add validators, open transaction ordering, or remove administrators. Validator distribution, stake concentration, client diversity, multisignature controls, and upgrade timelocks require separate review.

12.8 State Growth Can Become a Long-Term Bottleneck

Higher throughput creates accounts, contract storage, logs, and historical data more quickly. If pruning, snapshots, archiving, and synchronization cannot keep pace, node startup time and operating costs will continue to rise.

How Should You Evaluate a Parallel EVM Project?

First, identify the parallel model. Understand how transactions are assigned, how conflicts are detected, the granularity of rollbacks, and whether the system has a stable degradation path under high contention.

Second, examine determinism. Every node should commit state in the same order, and a project should disclose concurrency tests, client implementations, audits, and failure-handling mechanisms.

Third, inspect the state database. Examine random reads and writes, caching, state commitments, snapshots, synchronization, pruning, and archiving rather than focusing solely on CPU execution speed.

Fourth, distinguish ordering from finality. Determine when a transaction enters a block, when execution completes, when the state root forms, and when economic or protocol finality is reached.

Fifth, verify the scope of compatibility. Test wallets, RPCs, contract deployment, precompiles, events, indexers, debuggers, and infrastructure instead of treating “EVM compatible” as zero migration cost.

Sixth, observe real workloads. Compare independent transfers, hot contracts, complex DeFi, and network congestion while recording failure rates, re-execution rates, latency, and node resource use.

Hotcoin's Six-Dimension PAR-EVM Framework

The PAR-EVM checklist offers six dimensions for quickly evaluating a parallel EVM project. It breaks down technical and operational risks; it is not an investment rating or performance certification.

14.1 P: Parallelism — How Does Parallel Execution Work?

Determine whether the system uses advance declarations, static analysis, or optimistic execution; how it schedules transactions; how much work can genuinely reach multiple cores; and how it recovers from conflicts.

14.2 A: Access — How Much State Contention Exists?

Examine read and write sets, hot contracts, rollbacks, and re-execution rates. Average TPS does not reveal the capacity available when popular applications become congested at the same time.

14.3 R: Runtime — How Broad Is EVM Compatibility?

Test bytecode, RPCs, precompiles, gas behavior, tracing, wallets, indexers, and development tools. Compatibility should be demonstrated through actual testing rather than slogans.

14.4 E: Engine — Do Execution and the Database Work Together?

Evaluate the state database, asynchronous I/O, caches, state roots, snapshots, and synchronization. If disk access blocks the CPU, parallel execution cannot improve overall performance.

14.5 V: Validation — Who Verifies and Confirms?

Review consensus, validator count, hardware requirements, client diversity, and finality. Faster blocks cannot replace independent verification and fault tolerance.

14.6 M: Measurement — How Were the Results Measured?

Require disclosure of transaction types, hardware, node scale, contention ratios, block parameters, and test duration, and prioritize sustained results from real network workloads.

Frequently Asked Questions

15.1 Does a Parallel EVM Change Solidity Contract Results?

Not when implemented correctly. A parallel client must produce a final state equivalent to executing transactions in the canonical block order; it detects and re-executes conflicting transactions.

15.2 Can Every EVM Transaction Run in Parallel?

No. Transactions accessing different state are better candidates for parallelism. Transactions that read or write the same contract state have dependencies and usually require ordering, rollback, or recomputation.

15.3 Which Has Higher TPS, Monad or Sei?

Numbers cannot be compared fairly without the transaction type, hardware, node scale, and definition of finality. Compare throughput, latency, failure rates, and resource consumption under the same workload.

15.4 Is a Parallel EVM a Layer 2?

No. It is an execution optimization that can be used by an L1 or a Rollup. A network's layer depends on its settlement, consensus, and data-availability design.

15.5 Can a Popular DEX Slow Down Parallel Execution?

Yes. If many transactions contend for the same liquidity pool or global state, they create conflicts. Contract state design, pool separation, and batching all affect the available parallelism.

15.6 Does EVM Compatibility Mean MetaMask Works Immediately?

A compatible RPC can usually connect, but users must still confirm the chain ID, gas token, network parameters, and wallet support. Contracts and infrastructure also require separate testing.

15.7 Do Ordinary Users Need to Understand Parallel Scheduling?

They do not need to understand the scheduling algorithm, but they should examine real fees, confirmation times, network stability, bridging, administrator permissions, and wallet security instead of relying only on advertised TPS.

Conclusion: Parallelism Is a Systems Problem, Not a Single Switch

A parallel EVM computes non-conflicting transactions simultaneously, then preserves EVM semantics through conflict detection and deterministic commits. It can unlock multicore CPU capacity, but it cannot make transactions sharing the same state infinitely concurrent.

Monad combines optimistic parallel execution, MonadDB, MonadBFT, and a pipelined architecture to redesign the EVM node. Sei combines optimistic parallelization, SeiDB, and Twin-Turbo Consensus. Both demonstrate that high performance comes from optimizing execution, storage, propagation, and consensus together.

Viewed through the Web3 Technology Stack: From Base-Layer Blockchains to Applications, a parallel EVM primarily improves execution but remains constrained by consensus, data, nodes, and application state design. Evaluating a project requires attention to high-contention workloads, long-term state growth, and real decentralization.

Returning to The 2025 Web3 Frontier Landscape, readers can continue comparing parallel EVMs with modular blockchains, ZK technology, interoperability, oracles, and other scaling approaches.

To connect to Web3 applications with a self-custody wallet, consider Hotcoin Web3 Wallet. For mobile market data and trading tools, visit Hotcoin App. Browse more educational content on Hotcoin.

Risk warning: This article is for education and information only and does not constitute investment, trading, blockchain development, legal, or tax advice. Parallel EVM projects may face concurrent-execution errors, state conflicts, client vulnerabilities, node and validator centralization, database failures, bridge risks, contract upgrades, performance degradation, token volatility, and regulatory risks. Before participating, verify the latest official documentation, audits, mainnet parameters, validator and administrator permissions, real workload data, and exit mechanisms, and only commit assets you can afford to lose.