In the landscape of Layer-1 blockchains, Monad has emerged with a bold proposition: delivering high throughput and low latency without forsaking the familiar Ethereum Virtual Machine (EVM). Monad is an EVM-compatible Layer-1 chain that targets 10,000 transactions per second (TPS) and roughly one-second finality, addressing Ethereum’s scalability bottlenecks through novel design choices. By redesigning core components—consensus, execution, and state storage—Monad seeks to resolve the “blockchain trilemma” of achieving speed, security, and decentralization simultaneously. This article delves into Monad’s blockchain architecture, focusing on its asynchronous consensus (decoupling block finalization from transaction execution), optimistic parallel execution of transactions, and the custom MonadDB storage engine. We also compare Monad’s approach with similar techniques in other high-performance blockchains to put its innovations in context.

Decoupling Consensus and Execution (Asynchronous Execution)

Most traditional blockchains tightly couple the consensus process (ordering and finalizing blocks) with the execution of transactions. For example, in Ethereum, a proposed block’s transactions must be executed by validators before the block is finalized and propagated, which inherently slows down block production. This sequential coupling means each block’s contents are processed end-to-end (proposal -> execution -> validation) before moving to the next, creating a throughput bottleneck. Monad takes a different approach by decoupling consensus from execution, allowing these processes to occur in parallel.

In Monad’s design, the consensus layer (known as MonadBFT) focuses only on agreeing on an order of transactions, without waiting to execute them immediately. Blocks are proposed and finalized based on transaction ordering and cryptographic voting, while the actual execution of those transactions happens asynchronously in a separate pipeline. This means validators can continue to agree on new blocks even as prior blocks’ transactions are still being processed, maximizing CPU and network utilization. Monad’s consensus protocol is a custom Byzantine Fault Tolerant algorithm derived from HotStuff (dubbed MonadBFT), optimized for speed. It employs a pipelined two-phase commit that overlaps proposal, voting, and finalization for different blocks, achieving sub-second finality per block. In practice, Monad produces blocks on the order of 400–500 ms intervals and finalizes them in about 800 ms. This rapid cadence is supported by features like tail-fork resistance (to avoid losing blocks if a proposer is slow) and an efficient gossip protocol called RaptorCast for fast block propagation across the network. The end result is that consensus can run at high speed without being bottlenecked by execution delays.

Why is this asynchronous approach important? Decoupling consensus from execution lifts the usual constraints on block processing. Validators no longer need to halt block production while complex transactions execute. As Monad’s documentation explains, “traditional blockchains require blocks to be fully processed before the next consensus round can begin,” whereas Monad’s asynchronous execution lets consensus and transaction processing occur in parallel within each block time. This enables each block to utilize the full timeslot for either consensus or computation as needed, significantly improving throughput without compromising the determinism of results. Developers can include computationally heavy logic (e.g. complex DeFi trades, on-chain games, AI algorithms) in their contracts without worrying that they will stall the consensus process. In short, Monad’s asynchronous consensus design provides a flexible pipeline: the network rapidly finalizes the order of transactions, while the execution engine is free to churn through transaction processing at its own pace, often speculatively. This architecture lays the groundwork for the next major innovation: executing many transactions at once.

Optimistic Parallel Transaction Execution

Decoupling consensus gave Monad the freedom to rethink how transactions are executed. Instead of the Ethereum norm—executing transactions strictly one-by-one in the order they appear—Monad embraces parallel execution to fully leverage modern multi-core processors. The core idea is optimistic parallel execution: assume transactions do not conflict with each other and execute as many as possible simultaneously, then handle any conflicts after the fact. In practice, when a block of transactions is ready to be processed, Monad’s execution engine will speculatively run all transactions in parallel, treating them as if they operate on independent parts of the state. As each transaction executes, the engine records its read-set and write-set (the portions of state it accessed or modified) along with the tentative outcome. This per-transaction record can be thought of as a PendingResult, containing the transaction’s inputs (pre-state) and outputs (post-state). After this concurrent execution phase, Monad enters a commit phase where it serially validates and commits transactions in the original block order. One by one, each PendingResult’s recorded inputs are checked against the latest global state to verify that nothing has changed that would invalidate the result.

• If a transaction’s inputs are still valid (no other transaction committed before it has modified that part of the state), then its effects are applied to the global state immediately. This confirms the transaction as if it had executed sequentially, but it benefited from being pre-computed in parallel.
• If the inputs have been altered by a prior transaction, that means a conflict occurred. In this case, Monad will re-execute that transaction on the updated state to get the correct result before committing it. This ensures the final state is as if transactions had run in strict sequence, preserving deterministic correctness despite the parallel speedup.

This approach is termed “optimistic” because it optimistically assumes independence and only does extra work (re-execution) when a bad assumption is discovered. Fortunately, in many real workloads, most transactions do not conflict—think of hundreds of users sending tokens to different addresses or interacting with unrelated smart contracts. Those can safely execute concurrently, dramatically improving throughput. Even when conflicts occur (e.g. two trades on the same liquidity pool or two updates to the same account balance), the penalty is limited to re-running the conflicting transactions, which is often fast because the computations and signatures may be cached from the first run. Monad’s runtime even avoids repeating certain expensive steps on re-execution—like signature verification—which were already done before any state change and can be reused.

Why parallel execution matters: By executing many transactions at once, Monad can utilize all CPU cores and avoid the idle gaps of sequential processing. This removes a major performance ceiling present in traditional EVM chains, enabling higher TPS within a single block and allowing more complex applications to run smoothly. For example, on a sequential chain, if one transaction is computationally heavy, it delays all others; on Monad, that heavy transaction can run alongside other transactions on different cores. According to Monad’s team, this does not break determinism because the final committed order and results are equivalent to some sequential execution orde. The benefit is evident in use cases like on-chain order book exchanges, real-time games, or high-frequency trading dApps that demand many actions per block. Those transactions can now all settle together in one block without waiting for one another, as long as they target independent state. By eliminating the sequential bottleneck, latency and throughput improve — apps see faster execution and users get near-instant confirmations, all while writing contracts in the usual Solidity/EVM model. Monad’s optimistic parallelism thus brings a Solana-like performance boost to an EVM chain, a combination previously thought to require sacrificing compatibility or decentralization.

It’s worth noting that Monad isn’t the only project exploring parallel execution; however, its approach is distinguished by maintaining the standard Ethereum programming model. Developers do not need to annotate transactions with which parts of state they will touch, or learn a new smart contract language. Monad’s scheduler and conflict-detection logic handle parallelism under the hood, making it largely transparent to developers. This stands in contrast to some other blockchains, as we’ll discuss later, where achieving parallelism requires explicit structuring of transactions. Monad’s strategy aims to strike a balance: push the limits of parallel processing while keeping the platform developer-friendly and EVM-consistent.

MonadDB: A High-Performance State Database

Executing transactions faster and in parallel is only part of the challenge—state management (storage and retrieval of blockchain state) must also keep up. In Ethereum, reading and writing contract state (stored in a Merkle-Patricia Trie) is one of the most expensive operations, often bottlenecked by slow disk access or large memory requirements. To address this, Monad includes a custom-built database called MonadDB to optimize how state is stored and accessed. MonadDB is essentially a specialized key–value store for the blockchain’s state trie, designed from the ground up for speed and concurrency. Unlike Ethereum which uses a generic database (LevelDB/RocksDB) underneath the state trie, MonadDB natively implements the Merkle-Patricia Trie inside its storage engine. By doing so, it eliminates a layer of abstraction and inefficiency, reducing the overhead of each state lookup or update. In simple terms, MonadDB knows how to store accounts, contract storage slots, balances, etc., in a way that’s optimized for blockchain access patterns, rather than treating them as arbitrary data. This yields fewer disk reads and writes per operation and leverages in-memory caching for frequently accessed data. Consequently, when a transaction executes and needs to read some account’s balance or write a new storage value, MonadDB can serve that request faster than a general-purpose DB would.

Crucially, MonadDB is built to handle many state queries at once, aligning perfectly with Monad’s parallel execution model. It supports asynchronous, multi-threaded access to state: multiple CPU threads can fetch from or write to the database concurrently without stepping on each other. For example, if one transaction is reading an account balance and another is updating a different smart contract’s storage, MonadDB can process those in parallel. This design ensures that the gains from parallel CPU execution are not lost waiting on a single-threaded disk or memory bottleneck. State reads and writes become scalable operations, which is vital under high throughput. According to the Monad team, state queries are handled in a way that minimizes disk I/O and often serves data from memory caches when possible. Even writes are buffered and optimized so that committing a block’s worth of transactions doesn’t stall the system.

Another goal of MonadDB is lowering the hardware requirements for running a node. Traditional high-throughput chains (and even Ethereum to some extent) demand a lot of RAM to keep the state in memory for speed. MonadDB instead makes it feasible to store most of the state on a fast SSD without a severe performance hit. It accomplishes this through its efficient trie and caching scheme, meaning validators don’t need hundreds of gigabytes of RAM to avoid slow disk lookups. By shifting more state storage to disk (while still accessing it intelligently), MonadDB significantly reduces memory usage and node cost, which in turn helps decentralization by allowing more participants to run nodes on commodity hardware. The Monad team explicitly notes that this creates “a clear path to scalable decentralization” by lowering barriers to entry for validators.

MonadDB also streamlines node synchronization. A new node joining the network can use MonadDB’s state snapshot feature to rapidly get the latest state without replaying the entire chain history. In essence, a node can download a recent state trie (like grabbing a snapshot of the world state) and verify it against the known Merkle root on the blockchain, similar to how Git fetches only the latest changes. This state sync approach allows fast bootstrapping of nodes (useful when the chain state grows large over time) at the cost of trusting the snapshot source, which can be mitigated by cross-verifying with multiple peers. By making full nodes easier to run and sync, MonadDB again supports the network’s decentralization while handling the demands of high volume throughput.

In summary, MonadDB is the under-the-hood workhorse that keeps Monad’s ambitious execution model running smoothly. It combines a custom Merkle trie storage, low-latency caches, and concurrent access capabilities to ensure that reading/updating blockchain state isn’t the choke point in the system. This component is as critical as the parallel execution engine itself: without a fast state database, running 10,000 TPS would quickly grind to a halt due to database latency. By innovating at this level, Monad extends the performance gains to storage and makes high throughput sustainable.

Comparisons with Other Blockchain Approaches

Monad’s architectural choices – asynchronous consensus, parallel execution, and a custom state store – place it in a cohort of next-generation blockchains aiming to greatly scale throughput. However, different projects take very different approaches to the same fundamental problems. Let’s compare Monad’s design with some similar high-performance chains and techniques: Parallel Execution Models: Broadly, two strategies have emerged for parallelizing transactions on blockchains: (1) the “state access” method, and (2) the “optimistic” method. Monad uses the optimistic approach as described, which assumes independence and then fixes conflicts. In contrast, platforms like Sui and Solana use a state access (or object-based) method, where transactions must declare upfront which parts of state they will touch. By knowing the read/write sets in advance (or by structuring the state as independent objects), the system can schedule non-overlapping transactions in parallel deterministically. For example, Solana’s runtime (Sealevel) requires each transaction to specify which accounts it will read or write, and it then schedules transactions concurrently if they operate on different accounts. Similarly, Sui uses an object-centric model (with the Move language) where transactions on distinct objects (or with certain ownership rules) can bypass consensus or run in parallel, while conflicting ones are serialized. The advantage of the state access approach is that it avoids the need for any re-execution: the parallelism is planned so there will be no conflicts at runtime. It also enables interesting optimizations like more dynamic fee markets — a blockchain can detect “hot spots” in state (e.g. a popular NFT mint contract) and prioritize or charge differently for those transactions, since it knows exactly which transactions contend for the same state. The downside is increased complexity for developers: they must write contracts and transactions in a new paradigm, explicitly managing object locks or account lists, often in a new language (Move or Rust), which raises the barrier to entry.

Monad’s optimistic parallelization trades off that upfront complexity for a simpler developer experience. By handling conflicts retrospectively, Monad lets developers continue using Solidity and the EVM model without worrying about specifying dependencies. This is a strategic choice to attract the vast existing Ethereum developer community. For instance, Sui and Aptos (both Move-based chains emerging from the Meta/Diem project research) boast extremely high theoretical performance – often cited at 100,000+ TPS with sub-second finality – but they require developers to rewrite dApps in Move and follow strict composability rules. Sui’s object-based parallelism excels for use cases like simple asset transfers or game actions that fit into independent objects, but it struggles or becomes restrictive for complex DeFi scenarios where lots of contracts and shared state interact. Monad, on the other hand, preserves Ethereum’s composability (all contracts share one global state and can interact freely) while still significantly boosting performance. As one analysis put it, Monad aims to offer “Solana-level performance combined with Ethereum’s composability and developer familiarity”. Developers can port existing Solidity contracts to Monad with little or no changes, immediately gaining throughput benefits, whereas moving to Sui/Aptos would mean rebuilding in Move from scratch.

Another comparison is with Solana itself. Solana is a non-EVM L1 that achieved high TPS through a mix of parallel execution and other optimizations (like a fast proof-of-history clock). It requires coding smart contracts in Rust and handling accounts explicitly, and it historically demanded very powerful hardware for validators to keep up with its throughput. This led to some centralization concerns as not everyone can run a top-spec machine. Monad’s approach tries to avoid those pitfalls: it keeps hardware requirements modest by using MonadDB (store state on SSD, lower RAM) and aims for a larger validator set, while offering performance in the same ballpark as Solana. Ethereum itself has chosen a different path to scale: instead of parallelizing execution on the base layer, it leans on Layer-2 rollups and sharding for throughput, keeping the L1 deliberately simple and decentralized. Rollups (e.g. Optimism, Arbitrum) maintain EVM compatibility but they achieve only on the order of 50–100 TPS on-chain (with bursts handled off-chain). Monad’s philosophy is more monolithic: maximize performance at L1 so that using complex L2 constructions or new programming languages isn’t necessary. This does mean Monad has to prove it can scale and stay decentralized on its own; as of its testnet in 2025, the validator count and security are still growing, whereas Ethereum already has thousands of nodes and a mature security record. In summary, compared to its peers, Monad occupies a unique middle ground. It delivers multi-thousand TPS and sub-second finality akin to Solana, Sui, or Aptos, but crucially without abandoning the EVM. This could give Monad an edge in attracting existing Ethereum dApps that need more performance but don’t want to rebuild on a new chain or deal with fragmented liquidity and bridges. Other high-speed chains like NEAR or Avalanche also introduced new sharded designs or non-EVM VMs to scale, and while they achieve respectable throughput (hundreds of TPS with quick finality), none have demonstrated ~10k TPS on pure Ethereum bytecode as Monad claims. Monad’s bet is that by offering Solana-class scaling within the Ethereum ecosystem, it can bridge the gap between the two worlds: providing the speed and user experience of a “fast L1” while preserving the composability and developer base of Ethereum.

Conclusion

Monad’s blockchain architecture represents a significant reimagining of the EVM-based chain, engineered for performance. By decoupling consensus from execution, it removes artificial waits and allows the network to drive at full throttle in both agreeing on blocks and processing transactions. By executing transactions in parallel with optimistic concurrency control, it breaks the single-threaded throughput limit that has long capped Ethereum and similar chains. And by introducing MonadDB, it ensures that state storage and access won’t lag behind the increased transaction volume, all while lowering node resource requirements for greater decentralization. Together, these innovations enable Monad to claim throughput and latency numbers that would have seemed unattainable for an EVM chain a few years ago (on the order of 10k TPS, ~0.5–1s finality in tests). It’s important to note, however, that Monad is still a young project (testnet as of 2025) and faces challenges ahead. The true test will be sustaining its performance under real-world conditions and heavy decentralized usage, while maintaining security. Parallel and asynchronous execution add complexity to the protocol; thorough auditing and battle-testing are needed to ensure no subtle bugs or vulnerabilities emerge in edge cases. Additionally, Monad must grow its validator network and ecosystem. Ethereum’s strength lies not just in technology but in decentralization and community. Monad will need to demonstrate that its high throughput doesn’t come at the cost of security or decentralization as it scales up its validator set and hardens its consensus. Early indications are promising, and many eyes in the crypto community are watching whether Monad can validate its claims in practice and perhaps carve out a place as the go-to chain for high-performance dApps. In the broader context, Monad can be seen as an attempt to combine the best of both worlds: it offers the speed of newer monolithic chains and the familiarity of the Ethereum stack. Against Ethereum, it offers speed; against Solana, it offers compatibility; against Sui/Aptos, it offers familiarity. If Monad succeeds, it could prove that you don’t have to abandon the EVM or fragment into Layer-2s to achieve massive scaling – you can rebuild the engine of a blockchain to go faster while keeping the car intact. This balanced vision positions Monad as a promising contender in the L1 landscape, aiming to bring millions of users and complex applications on-chain without the usual trade-offs. As the project matures and moves toward mainnet, it will demonstrate whether its architectural innovations truly deliver “speed without sacrifice” in the long run.