Monad Cryptocurrency Explained: Technical Deep Dive and Comparison with Ethereum and Solana
Monad is a high performance EVM compatible Layer 1 designed to combine Ethereum style smart contract compatibility with Solana like speed. This guide explains Monad architecture, parallel EVM execution, asynchronous execution, MonadBFT consensus, RaptorCast propagation, MonadDb storage, and the practical differences versus Ethereum and Solana.
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Monad Cryptocurrency Explained: Technical Deep Dive and Comparison with Ethereum and Solana
Introduction: why Monad exists
Every cycle brings a new batch of fast blockchains. The hard part is not claiming speed, it is delivering speed while keeping the things developers already rely on: a mature wallet ecosystem, familiar developer tooling, and a predictable execution environment.
Ethereum remains the largest smart contract platform by ecosystem gravity. Its base layer produces blocks on a twelve second cadence. Ethereum’s execution model is intentionally conservative: transactions in a block are strictly ordered and the network converges on a single canonical history. That model makes Ethereum reliable for global settlement, but it also means scaling the base layer directly is difficult.
Solana proved a different point. When fees are tiny and confirmations feel instant, new categories of apps appear. Solana’s runtime is designed to execute many smart contracts in parallel when they do not touch the same state, and Solana aims for very short slots in normal operation.
The gap is obvious. Ethereum has the EVM and the developer network effects. Solana has the performance and a consumer friendly user experience. Monad is built to live in that gap: keep Ethereum compatibility, redesign the stack for parallelism and pipelining, and push latency down while keeping determinism.
What is Monad and what is the MON token:
Monad is an EVM compatible Layer 1 smart contract platform. The official developer documentation states that public mainnet launched on November 24, 2025 and that the chain is designed for performance and scalability on commodity hardware.
MON is the native token that powers the Monad network. According to Monad Foundation, MON is used for transaction fees and staking, with an initial supply of 100 billion tokens at the launch of Monad public mainnet. The same tokenomics overview also describes a fee model where the base component of transaction fees is burned and a block reward mints new MON for validator rewards.
In other words, when people say Monad cryptocurrency, they are usually referring to the MON token and the Layer 1 network it secures and powers.
Parallel EVM explained in two parts:
People often say parallel EVM as if it is one feature. It is more useful to treat it as two requirements that have to be true at the same time.
First requirement: parallel processing
Parallel processing means the chain can execute multiple transactions concurrently when those transactions do not depend on each other. In a computing analogy, it is the difference between a single lane toll booth and a multi lane toll plaza. If cars are headed to different exits, they should not have to wait for one shared line.
Second requirement: EVM compatibility
EVM compatibility means existing Ethereum style contracts, wallets, and developer tools still work. Solidity code should compile and deploy. Ethereum JSON RPC should behave predictably. The chain should preserve the semantics developers expect from ordered EVM execution.
Solana demonstrates the parallel processing requirement at scale. Its Sealevel runtime can process transactions in parallel by using the fact that Solana transactions describe the state they will read and write, allowing non overlapping transactions to run concurrently across cores.
The challenge is that the EVM was not designed for that. EVM transactions can touch state dynamically, and you generally do not know the full read and write footprint until you run the transaction. This is why a parallel EVM is a real engineering problem, not a marketing label.
Monad’s architecture: what is technically different
Monad’s performance story is a full stack set of design choices. The main ideas are parallel execution, asynchronous execution, a consensus protocol called MonadBFT, efficient block propagation via RaptorCast, and a storage engine called MonadDb.
Parallel execution through optimistic concurrency control
Monad executes transactions in parallel while preserving Ethereum semantics. The Monad docs make a strong claim here: Monad blocks are the same kind of object as Ethereum blocks, a linearly ordered list of transactions, and executing that list yields the same final result as it would on Ethereum.
So how can it run in parallel without changing the result:
Monad uses optimistic execution. It starts executing later transactions before earlier transactions in the block have fully completed, and it tracks which inputs each transaction read. If an earlier transaction in the ordered list later changes an input that a later transaction relied on, that later transaction is re executed with the correct state.
A useful mental model is a two phase workflow:
Phase one:
Execute many transactions concurrently and record what each transaction read and wrote.
Phase two:
Merge state updates sequentially in the original transaction order, detect conflicts, and re-execute only the transactions that ran with stale inputs.
This approach borrows from optimistic concurrency control and transactional memory concepts, but the key takeaway for application developers is simpler: you get parallel speed when transactions are independent, and you still get Ethereum correctness when transactions conflict.
Asynchronous execution and why pipelining matters
Parallel execution increases how much work you can do per unit time. But many blockchains still waste most of their block time because execution and consensus are interleaved on the critical path.
Monad explicitly decouples consensus from execution, moving execution out of the hot path of consensus into a separate pipeline. In Monad, nodes can come to consensus on the official ordering of transactions without executing those transactions during the consensus process. After a block is finalized, nodes execute the transactions to produce the agreed upon state.
This is a major contrast with Ethereum, where a block includes a post state root and validators need to execute transactions to validate that root. Monad’s docs argue that interleaving execution with consensus shrinks the execution budget dramatically, while asynchronous execution lets execution use the full block time in parallel with consensus. This design choice cascades into concrete protocol differences.
Monad charges transactions based on gas limit rather than gas usage, and it uses a reserve balance mechanism to ensure safety under asynchronous execution. Monad also does not have a global mempool, forwarding transactions to the next few leaders for efficiency.
If you want a single sentence summary, Monad’s pipeline tries to keep the network agreeing on ordering quickly while the execution engine stays busy using the full available time and cores.
MonadBFT consensus for low latency finality
Throughput is not only an execution problem. The chain also needs fast agreement on blocks, and it needs to deliver blocks to validators quickly.
Monad’s consensus protocol is MonadBFT. The official documentation describes it as a Byzantine fault tolerant consensus mechanism designed to support high throughput, low time to finality, and a large validator set. In Monad’s published configuration, the minimum block time is 400 milliseconds, speculative finality is one slot, and full finality is two slots, about 800 milliseconds.
The same documentation emphasizes tail forking resistance as a key property of MonadBFT, positioning it as protection against a class of MEV attacks and instability that can arise in pipelined leader based BFT designs.
RaptorCast for efficient block propagation
High throughput implies large blocks. Large blocks imply a propagation problem: a single leader cannot be expected to upload multi megabyte blocks directly to hundreds or thousands of validators at low latency without becoming a bottleneck.
Monad uses RaptorCast to propagate block proposals in MonadBFT. The developer docs describe RaptorCast as a multicast protocol where leaders erasure code block proposals into chunks using a Raptor code and distribute them through a two level broadcast tree, allowing validators to reconstruct the proposal once enough chunks arrive.
It is the same principle as splitting a large file into pieces and using redundancy so that the network can recover even if some pieces are delayed or lost. The benefit is lower latency distribution without requiring a single node to carry all the bandwidth load.
MonadDb: storage designed for parallel EVM execution
On high throughput chains, storage quickly becomes the limiting factor. If every state read forces a blocking disk lookup, you lose the benefits of parallel execution.
MonadDb is a custom built key value database optimized for storing authenticated blockchain data while maintaining Ethereum compatibility. It is specifically optimized for storing Merkle Patricia Trie nodes on disk, and it implements a Patricia Trie structure natively rather than embedding trie nodes inside a generic database structure.
Because Monad executes multiple transactions in parallel, the docs emphasize that reads should not block continued operation. MonadDb therefore uses asynchronous I O and leverages kernel support such as io_uring on Linux.
Monad vs Ethereum: what changes for developers and users
It is tempting to say Monad is Ethereum but faster. The better technical framing is: Monad preserves Ethereum execution semantics but changes the architecture and several protocol level behaviors to achieve higher throughput and lower latency.
Block time and finality expectations
Ethereum produces blocks every twelve seconds. Ethereum also has an explicit notion of finality under proof of stake, and Ethereum’s own roadmap notes that it takes about fifteen minutes for an Ethereum block to finalize today, which is why single slot finality is a major research direction.
Monad’s published configuration targets a 400 millisecond minimum block time and full finality in two slots, about 800 milliseconds, under MonadBFT.
Parallelism without breaking Ethereum semantics
Ethereum’s base layer EVM execution is sequential. Monad keeps the same ordered list model but executes in parallel and guarantees the final result is identical to sequential execution of the same ordered list. That is the core of what people mean by a parallel EVM.
Transaction charging, gas safety, and the reserve balance mechanism
Monad introduces a different safety model for gas under asynchronous execution. The Monad docs list several developer visible differences.
Transactions are charged based on gas limit rather than gas usage, and consensus and execution use a reserve balance mechanism to ensure that transactions included in consensus can be paid for during execution.
Monad does not support EIP 4844 type 3 blob transactions. Monad also adjusts some opcode and precompile pricing and increases the maximum contract size to 128 kb.
Mempool and transaction propagation
Ethereum’s public mempool and builder ecosystem are central to its block building dynamics. Monad takes a different approach: it has no global mempool and forwards transactions to upcoming leaders for efficiency.
Historical state access
High throughput implies rapid growth of state and history. Monad documentation explicitly warns that full nodes do not provide access to arbitrary historic state because it would require too much storage at high throughput.
For some applications, this means you plan for indexing and data services as first class infrastructure, rather than assuming arbitrary historical queries against every node.
Monad vs Solana: similar performance goals, different execution semantics
Solana and Monad both take parallelism seriously, but they arrive at it through very different programming models.
Solana’s explicit account model
Solana’s Sealevel runtime can process many smart contracts in parallel, and Solana explains that the key enabler is that transactions describe all the state a transaction will read or write while executing. This makes scheduling more deterministic: non overlapping transactions can be placed in different execution lanes without any need for speculative re execution.
Solana’s own documentation also highlights that slots are configured to last about 400 milliseconds, with some fluctuation.
Solana fees: base and prioritization, plus localized congestion effects
Solana fees are split into a base fee and an optional prioritization fee. The official Solana documentation explains that the base fee is paid per signature, and that the prioritization fee can be set via compute unit price and compute unit limit to increase the chance that the current leader processes a transaction.
In the ecosystem, this is often discussed as a form of localized congestion pricing: when a specific program or set of accounts becomes a hotspot, prioritization fees rise for transactions competing over that hotspot while unrelated activity can remain cheap. The details of how well this behaves in practice have evolved over time, and there is active research and debate on the topic.
Monad’s optimistic approach inside the EVM
Monad cannot require EVM transactions to declare their full read and write footprint up front without changing the programming model. Instead, Monad uses optimistic parallel execution: run transactions in parallel, track inputs, and re execute when conflicts are discovered, while still committing results in the original serial order.
That is the central difference in semantics.
Solana gives the runtime a scheduling map through declared accounts.
Monad gives the runtime freedom to speculate, then correct itself while preserving Ethereum semantics.
Developer experience and portability
Solana developers build within Solana’s program and account model. Monad targets Ethereum developers. Because Monad is EVM compatible, the migration path for many Ethereum applications is closer to redeployment than rewrite, and developers keep familiar wallets and tools. Monad’s mission statement emphasizes full compatibility with the existing Ethereum ecosystem.
If you are choosing between Monad and Solana, the practical question is not only speed. It is whether you want Solana’s explicit account based programming model or the EVM model with parallelism implemented under the hood.
A practical framework for evaluating Monad:
Monad is now a live network, so evaluation is less hypothetical than it was in early devnet discussions. Here are a few concrete technical questions that help teams decide whether Monad is a good fit.
How parallel is your workload
If your application naturally partitions state across many contracts and many users, Monad’s optimistic parallel execution has more room to find concurrency. If everything hits one hot contract, you will still benefit from low latency consensus and fast storage, but you should expect less parallel scaling.How latency sensitive is the user experience
If you are building trading, gaming, social, or payment flows where users care about confirmation speed, Monad’s 400 millisecond slot configuration and sub second finality targets are directly relevant.How dependent are you on Ethereum tooling
If your stack assumes Solidity, Ethereum JSON RPC, and popular wallets, Monad is designed to be a natural target. If you are comfortable in Solana’s environment and want to use its native programs, Solana remains a strong choice.What data requirements do you have
High throughput changes the economics of historical data. Monad documentation is explicit about limits around arbitrary historic state access on full nodes. If you need rich history queries, plan for indexing, archival services, or specialized infrastructure from the start.Do you consume real time blockchain data
Asynchronous execution and speculative finality introduce nuance for real time feeds. Monad provides documentation on speculative real time data and how consumers should interpret speculative execution before finality confirms the canonical outcome.
Conclusion
Monad is best understood as a parallel EVM Layer 1 that tries to push EVM performance without giving up Ethereum semantics. It does that through several connected choices.
Optimistic parallel execution: execute many EVM transactions concurrently, then commit in serial order with re execution on conflicts.
Asynchronous execution: decouple consensus from execution so consensus is not blocked by execution and the execution pipeline can use the full block time.
Fast consensus and propagation: MonadBFT targets low latency finality and RaptorCast distributes large blocks efficiently.
Storage built for throughput: MonadDb stores authenticated state efficiently with asynchronous I O and native trie structures.
Compared with Ethereum, Monad changes block time expectations and introduces protocol level differences like gas limit based charging, reserve balance safety, and a leader forwarded transaction flow.
Compared with Solana, Monad targets similar speed while staying inside the EVM compatibility set, using optimistic conflict detection instead of requiring explicit account access declarations.
