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Monad EVM throughput solves Ethereum’s serial execution limits with safe parallel processing. Read on to learn more.
Unlike the Monad blockchain, Ethereum was never designed for scalable execution at the runtime or execution layer. Blocks confirm quickly, and transactions settle reliably. But once they hit the EVM, things slow down because execution still runs one transaction at a time. EVM throughput is the bottleneck at the heart of every congestion problem.
Yet, the Monad EVM throughput solves this differently. It doesn’t break the EVM or force developers to rewrite smart contracts. Instead, it changes how transactions are scheduled; executing them in parallel when they don’t conflict, without compromising determinism or compatibility. This article touches on how the Monad blockchain achieves all that and more.
What are EVM Throughput Concerns?
Throughput in EVM chains isn’t about how fast blocks are produced. It’s about how many transactions the chain can execute per second, once the block is finalized. The problem? Execution is serial, at least in Ethereum.
The EVM itself isn’t inherently serial. It’s deterministic, meaning Same input + Same state = Same output. However, Ethereum’s implementation of the EVM processes transactions sequentially by design to avoid state conflicts. That’s not a flaw in the EVM spec; it’s a conservative choice by Ethereum clients.
So even if transactions are independent, say, one is a token swap and the other is minting an NFT, they’re still processed one after another. Ethereum assumes any transaction might interfere with another, directly impacting EVM performance and forcing it to queue every transaction.
To break past that ceiling, you need a runtime that can analyze state access in advance, identify non-conflicting operations, and safely run them in parallel, without sacrificing determinism. That’s exactly where Monad enters.
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How Monad Handles Execution Limits
Ethereum’s EVM processes transactions one after another, not because it lacks the computing power, but because it assumes every transaction might interfere with the next. Simply put, Ethereum values safety over everything else.
Monad breaks that boundary using parallel execution. And it’s not just by guessing. It is by analyzing each transaction’s behavior before it runs. Here’s how it works:
Let’s say a block contains four transactions:
Alice swaps USDC for ETH on Uniswap Pool A
Bob mints an NFT from a new collection
Charlie transfers ETH to Dave
Eve also swaps on Uniswap Pool A
Monad reads the inputs and predicts which parts of the blockchain’s state each transaction will access. Because the EVM is deterministic, Monad knows that:
Alice and Eve are touching the same Uniswap Pool A, so their transactions interact with the same state (liquidity reserves).
Bob and Charlie operate on entirely separate contracts and accounts, with no overlap with each other or with Alice and Eve.
Based on that:
Tx1 (Alice) and Tx4 (Eve) are queued sequentially because they touch the same state.
Tx2 (Bob) and Tx3 (Charlie) are executed in parallel; they’re independent, so there’s no conflict.
This scheduling model unlocks high Monad EVM throughput without changing contract behavior. Here is how:
The Monad blockchain preserves contract logic while reengineering execution throughput. Note that it still doesn’t introduce speculative execution. It just makes smarter decisions about which transactions can safely run side-by-side. Because execution is still deterministic: Same input + Same state = Same output, every node in the network reaches the same result, no matter how the work is divided.
This is where Monad EVM throughput gets its edge: not from faster blocks or bigger gas limits, but from parallelizing work the EVM already knows how to do. And at the core of this is Monad’s Parallel Execution model.
Understanding Monad’s Parallel Execution Model
Monad enhances transaction throughput by analyzing the interactions of each transaction with the blockchain state before execution. This analysis constructs a dependency graph, identifying which transactions can be processed simultaneously without conflicts.
For example:
Tx1: Dana stakes tokens in a contract.
Tx2: Raj claims rewards from a different staking pool.
Tx3: Kim votes on a DAO proposal.
Tx4: Lee stakes tokens in the same contract as Dana.
In this scenario, Tx1 and Tx4 interact with the same contract and are linked in the graph, requiring sequential execution. Tx2 and Tx3 operate on separate parts of the state and can be executed in parallel.
Monad's deferred execution decouples consensus from execution. The network first agrees on the transaction order using optimistic consensus. After finalizing the block, Monad schedules execution based on the dependency graph, grouping non-conflicting transactions into threads assigned to different processing cores.
This pipelined execution ensures efficient resource use, maintaining determinism and consistent final states across the network. By identifying safe opportunities for parallelism, Monad maximizes processing efficiency and scalability.
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How Monad Helps Developers Build Faster
Most smart contract developers don’t face execution limits until they try to scale. Then things slow down. Blocks fill up, users wait, and simple on-chain actions stall. This isn’t network lag. It’s a throughput ceiling.
Monad changes that. Enabling safe parallel execution removes the bottleneck of serial processing. Throughput becomes elastic and tied to compute power, not limited by one-by-one execution.
You can build fully on-chain games on Ethereum, but only if they’re lightweight, turn-based, or support very few players. Every move, attack, or trade is a transaction. Ethereum processes each one in order. As usage increases, latency climbs, and gas costs spike. The game slows down.
Monad makes real-time, on-chain interactivity possible by scaling execution with demand.
How Monad Differs From Other EVM Chains
Most EVM-compatible chains try to scale by reducing block times, increasing gas limits, or relying on rollups to offload execution. These methods help temporarily but don’t solve the core problem: the EVM still processes transactions individually.
Monad takes a different approach. It doesn’t work around the bottleneck. It removes it. Instead of pushing execution off-chain like rollups, Monad keeps it on Layer 1 and distributes it across multiple threads using a deterministic scheduler. This raises throughput without fragmenting state or sacrificing composability.
Chains like Solana or Sui use new languages to achieve speed. Monad keeps the standard EVM runtime. Developers still write in Solidity and use familiar tools, but behind the scenes, Monad runs that logic on a parallel execution engine, not a serial one.
Monad isn’t just faster. It scales vertically by making the EVM itself more efficient. That sets it apart from L2s, alt-EVMs, and monolithic chains trying to stack patches on top of a slow foundation.
Closing Thoughts
Monad EVM throughput doesn’t rely on shortcuts. It unlocks the EVM’s full potential by running transactions in parallel instead of in sequence. The logic stays the same, but performance scales with available processing power.
For builders aiming to scale, execution becomes the foundation. Serial processing no longer needs to be the limit. With Monad, parallel execution is no longer theoretical. It's real, reliable, and ready for production.
FAQs
Does Monad require developers to change how they write smart contracts?
No. Monad is fully EVM-compatible. You write in Solidity, deploy with the same tools, and the logic stays unchanged. The difference is in how that logic gets executed.Can parallel execution cause race conditions or non-deterministic results?
No. Monad’s execution is still deterministic. It analyzes transactions before they run and only parallelizes those that don’t touch shared state. Final state consistency is always preserved.Is Monad a rollup or a Layer 2?
Neither. Monad is a Layer 1 blockchain. It doesn’t rely on Ethereum for security or settlement. It provides native EVM compatibility with a redesigned execution layer.What types of apps benefit most from Monad’s execution model?
Any app with high-frequency, multi-user activity: live trading, real-time games, on-chain auctions, and complex DeFi logic. Monad’s throughput makes these responsive without compromising structure.
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