What Is Monad: How We Learned to Stop Worrying and Start Loving Parallel Evm

A Validator’s Journey Into the Architecture That Can Deliver 10,000 TPS

At a Glance:

• The Promise: 10,000 TPS with 2-second finality and full EVM compatibility
• What Makes It Different: MonadBFT consensus prevents validator gaming, parallel execution that handles conflicts, and purpose-built storage layer (MonadDB)
• P2P.org's Verdict: After two years on testnet with zero slashing, Monad is the first high-performance EVM chain that delivers in practice, not just theory
• For Institutions: Hardware requirements comparable to Ethereum, but with enterprise-grade performance and MEV resistance built into consensus

Three years ago, we watched Solana validators melt down under load. Two years ago, we saw Avalanche's C-Chain grind to a halt during a popular NFT mint. Last year, we observed BSC validators desperately cranking up gas limits only to watch their nodes fall further behind. Every "Ethereum killer" followed the same pattern: promise the moon, deliver a cratered landscape of compromises.

Then we started validating on Monad's testnet. And something new happened — it actually worked.

Why Every Fast Blockchain Faces the Same Issues

When you're running a validator on a "high-performance" EVM chain and Block time is 1 second, everything is going smoothly while the network is humming along at 500 TPS. Then PancakeSwap launches a new farm, or a hyped NFT collection starts minting. Suddenly, every transaction wants to touch the same contract. Your parallel execution engine, so proud of its 32 cores, watches 31 sit idle while one core desperately processes a queue of dependent transactions.

This is the little-known fact of parallel execution: it only works when transactions don't interact. The moment everyone rushes for the same exit, even a supercomputer becomes a Pentium.

We've seen this scenario play out many times before. We know how it ends. So when Monad claimed they'd solved it, at P2P.org we were skeptical. At first, we read their architecture. Then we tested it, and this is how we realized they'd done something genuinely smart that pushes the boundaries of blockchain technology forward.

The Consensus Evolution

Every blockchain consensus protocol since 2018 has been a variation on HotStuff — linear communication complexity, pipelined rounds, rotating leaders. It's elegant, proven, and fundamentally exploitable.

Here's the attack we've been watching for years, but nobody seems to talk about: Leader N+1 can always kill Leader N's block. Just refuse to include its certificate. Boom — Leader N loses their block rewards, their transactions get stolen, and Leader N+1 makes bank on the MEV. We call it tail-forking, and it's been happening on mainnet chains for years. The victims just don't realize it.

MonadBFT represents a totally new school of thought.

Only on Monad: Speculation Without Trust

Traditional BFT makes you wait for absolute certainty. Two rounds of voting, 2f+1 confirmations, carved in stone. MonadBFT says, "What if we didn't?"

When you receive a block proposal, you vote immediately. When you see that vote certificate (QC) come back (just one round) you speculatively execute. But here's the genius part: you also save a "tip," the header of what you just voted for. If the next leader tries to fork away your block, your tip becomes evidence. The subsequent leader sees it in the timeout certificate and must repropose your block or prove nobody has it.

The math is beautiful. If 2f+1 validators voted for a block, at least f+1 are honest. Those f+1 honest validators have the tip. Any timeout certificate must include at least one honest tip. The chain can't progress without including that block.

The result? Once an honest leader proposes a block, it can only be excluded if that leader equivocates (double-signs), which is slashable. The MEV honeypot just became a trap.

The Monad Way Is Also Faster

By speculating after one round instead of waiting for two, MonadBFT achieves 3δ latency (three network hops) versus HotStuff's 7δ. In real networks where δ is 50-100ms, that's the difference between 150ms and 350ms to finality.

But the real magic happens on the unhappy path. When leaders fail, HotStuff variants either sacrifice responsiveness (HotStuff-2) or communication complexity (Fast-HotStuff). MonadBFT keeps both by using Bracha's reliable broadcast — validators amplify timeout messages, achieving view synchronization in 2∆ without waiting for worst-case timeouts.

We've triggered thousands of leader failures on testnet. Recovery is consistently sub-second. No other BFT protocol achieves this combination of speed, efficiency, and resilience.

Monad’s Execution Layer That Shouldn't Work (But Does)

Decoupled Consensus and Execution

In Monad, validators agree on transaction ordering without executing those transactions. Execution happens asynchronously, lagging consensus by three blocks. This sounds insane until you realize a fundamental truth: the outcome is determined the moment ordering is determined. Execution just reveals what already is.

Think about it. Once transactions are ordered, their results are deterministic. Whether you execute them now or in three blocks doesn't change the outcome. It just changes when you learn the outcome.

This decoupling does something completely new: it gives execution the full block time instead of fighting consensus for milliseconds. In Ethereum, execution gets ~100ms out of a 12-second block. In Monad, execution gets the full 2 seconds while consensus runs in parallel. That's a 20x increase in execution budget without changing the block time.

The State Root Dilemma

But wait… if you don't execute transactions, how do you know the state root for the block?

You don't. And that's fine.

Monad includes a delayed merkle root from three blocks ago. If validators diverge in execution, they'll produce different roots. Three blocks later, when that root appears in a proposal, the divergent validator gets kicked out of consensus. They roll back to the last verified state and re-execute.

We've intentionally corrupted the state on our test validators to trigger this. Recovery is automatic and takes seconds. The delayed verification provides eventual consistency without sacrificing immediate progress.

A Parallel Execution Engine That Can Scale

Every parallel execution engine faces the same problem: transactions aren't naturally parallel. Alice pays Bob, Bob pays Carol, Carol pays Alice. Execute these in parallel and you'll get the wrong answer. Execute them sequentially and why did you buy that 64-core processor?

Monad's solution is wonderfully simple: execute first, apologize later.

Optimistic Execution: Fortune Favors the Bold

Transaction 1 reads Alice's balance and adds 100. Transaction 2 reads Alice's balance and subtracts 50.

Traditional parallel execution would lock Alice's account, forcing sequential processing. Monad says, "screw it, run both simultaneously."

Transaction 2 starts with Alice's original balance, not knowing that Transaction 1 is about to change it. Both transactions are complete. Then comes the reconciliation.

Monad checks: Did Transaction 2 read the state that Transaction 1 modified? Yes? Transaction 2's results are discarded, and it runs again with Transaction 1's updates. This continues—Transaction 3 might depend on Transaction 2's corrected results, causing a cascade of re-execution.

Sounds inefficient? It isn't, because a vast majority of transactions don't conflict.

The Parallelization Myth Everyone Believes

Most blockchain teams think parallel execution is the answer. Add more cores, run transactions simultaneously, watch the TPS counter explode. Except it doesn't work that way.

Multiple studies analyzing millions of Ethereum transactions tell a consistent story. Sei Protocol examined 2.49 million transactions and found 64.85% could execute in parallel with zero conflicts. Khipu's mainnet analysis showed 80% parallelizability. Block-STM demonstrates this in practice: 16-20x speedup with low contention, degrading gracefully to 3-4x when conflicts increase.

The math looks beautiful. Then you hit production and discover the truth.

State access consumes 70% of execution time (Flashbots research). You can parallelize computation all you want; you're fighting over the 30% that doesn't matter. Is signature verification, the operation everyone assumes is expensive? Less than 1% of total processing time. The actual killer is reading from and writing to the Merkle Patricia Trie.

Think about what this means. Every SLOAD operation becomes a randomized treasure hunt through a database that wasn't built for concurrent access. Block-STM proved the pattern: exceptional speedups with independent transactions, but even their optimized engine hits walls when everyone queues for the same state.

This is where Monad actually innovates.

When transactions conflict and require re-execution, the expensive work doesn't repeat. Signature results cache. The state stays in memory. JIT compilation persists. Only the state transitions run again. But the real breakthrough goes beyond handling conflicts, and focuses on eliminating the bottleneck everyone else accepts as inevitable.

Monad's own engineering team said it plainly: "Parallelization alone made little impact on performance because the bottleneck is state access."

The Storage Layer

Databases are where blockchain dreams go to die. You can have the fastest consensus, the cleverest execution, but if your disk can't keep up, you're building a very expensive space heater.

Ethereum clients use LevelDB or RocksDB — general-purpose key-value stores designed in 2011 for Facebook's social graph. They're embedding a Merkle-Patricia Trie into a B-tree or LSM-tree. It's trees all the way down, and every level adds overhead.

MonadDB said, "What if we just... didn't?"

Patricia Tries: All the Way Down

MonadDB implements Patricia Tries natively. Not as a data structure stored in a database, but as THE database. When you query the state, you're traversing the actual on-disk trie structure, not some interpretation of it.

This sounds simple but its implementation isn’t. It requires rethinking everything:

Asynchronous I/O: With parallel execution, dozens of transactions query state simultaneously. MonadDB uses io_uring (Linux's newest async I/O interface) to handle thousands of concurrent disk operations without thread spawning overhead.

Filesystem Bypass: Files are an abstraction. Filesystems are an abstraction on top of an abstraction. MonadDB says "Give me the raw block device." No filesystem fragmentation, no metadata overhead, no buffer cache confusion. Just bytes on disk exactly where MonadDB put them.

Persistent Data Structures: Updates don't modify existing trie nodes. They create new versions. This enables lock-free reads (critical for parallel execution) while maintaining atomicity. Old versions get garbage collected asynchronously.

The performance difference is staggering. Our benchmarks show 10x improvement in random state access versus RocksDB. But the real win is write amplification—MonadDB reduces it by 3x, extending SSD lifespan and reducing infrastructure costs.

RaptorCast and the Art of Multiplying Bandwidth

Here's a fun math problem: You need to send a 2MB block to 100 validators. Your upload bandwidth is 1 Gbps. How long does it take?

Traditional broadcast: 2MB × 100 = 200MB = 1.6 seconds. Your 2-second block time just died.

RaptorCast doesn't multiply — it divides.

The Elegant Violence of Erasure Coding

Instead of sending the full block to everyone, RaptorCast uses Raptor codes (RFC 5053) to create 300 encoded chunks where any 100 chunks reconstruct the original block. Each validator gets 3 chunks (weighted by stake). Then — and this is the clever part — validators share chunks with each other.The math:

• Leader upload: 3 chunks × 100 validators = 300 chunks ≈ 2MB (same as original block!)
• Propagation time: 2 network hops (leader → validators → validators)
• Failure tolerance: 33% of validators can be Byzantine, 20% packet loss, still recovers

But the real innovation is in failure recovery. If a block doesn't decode, validators can prove it with the malformed chunks. The leader can't claim "you didn't receive it correctly", as the cryptographic proofs make failures attributable.

We've tested RaptorCast under adversarial conditions — 50% packet loss, coordinated Byzantine validators, and network partitions. It recovers every time. The redundancy auto-adjusts based on observed failure rates. It's an antifragile infrastructure.

Why We Believe in Monad

We've validated on 40+ networks. We've seen every flavor of failure. Chains that work in simulation but die under load. Chains that scale perfectly until someone deploys Uniswap. Chains that achieve 100,000 TPS (on a private network, with one validator, processing empty transactions).Monad is different because it solves the right problems:

1. Consensus that can't be gamed: MonadBFT's tail-forking resistance means validators can't reorder history for profit
2. Execution that uses your hardware: Parallel execution that actually works when transactions interact
3. Storage that scales with SSDs: MonadDB turns random I/O into sequential writes
4. Network distribution that multiplies bandwidth: RaptorCast makes 100 validators share the load like 10,000
5. Compatibility without compromise: Full EVM bytecode compatibility, not "almost EVM" promises

The Validator's Verdict

After two years on testnet, here's what we know:

The Good:

• Consistent 10,000 TPS with real DeFi transactions
• 150ms finality (3x faster than Ethereum's 12-second blocks)
• Hardware requirements comparable to Ethereum (not Solana's supercomputer demands)
• MEV resistance is built into the consensus

The Challenge:

• State growth at 10,000 TPS means 50GB+ daily
• 2-second blocks require sub-second monitoring
• Parallel execution benefits from 32+ CPU cores
• RaptorCast needs quality network providers (packet loss hurts)

The Verdict: Monad delivers on its promises. Not through magical breakthroughs but through systematic engineering. Every bottleneck identified, analyzed, and eliminated.

What This Actually Means for Stakers

Before we get into institutional specifics, let's translate the technical architecture into practical outcomes:

For Everyone Staking MON:

Higher, More Reliable Yields
10,000 TPS means more network activity. More activity means more fees. More fees mean higher staking rewards. But only if your validator can actually capture them—which requires infrastructure optimized for Monad's architecture.

Faster Reward Compounding
2-second finality means staking rewards compound multiple times per minute, not once per day. Over time, this creates measurably better returns compared to slower chains.

Lower Slashing Risk
Parallel execution reduces validator race conditions that cause most slashing events on other chains. MonadBFT's design makes it nearly impossible for honest validators to get penalized.

Shorter Unstaking Periods
Approximately 5.5 hours from unstake to withdrawal. Compare this to Ethereum's 2-5 days. Liquidity when you need it, security when you don't.

For LST Protocol Users:

The liquid staking protocols we're integrated with (aPriori, Kintsu, Magma, Fastlane) each leverage Monad's performance differently—from MEV capture to distributed validator technology. But they all depend on validator infrastructure that can keep pace with 10,000 TPS.

For Institutional Allocations:

The technical architecture translates to risk mitigation. When you're allocating significant capital, you need validators who've stress-tested the edge cases, optimized for the specific bottlenecks, and built monitoring for Monad's unique requirements.

That's where institutional-grade infrastructure separates from commodity validation services.

What This Means for Institutional Staking

We don't chase hype. We've turned down validator opportunities on chains with bigger treasuries and louder marketing. We validate where the technology deserves it.

Monad deserves it.Their architecture is fast and correct in practice, not just theory. And not only on testnet, but under adversarial conditions.

When mainnet launches, we'll be running validators on hardware specifically optimized for Monad's architecture. NVMe arrays configured for MonadDB's access patterns. Network topology optimized for RaptorCast. Monitoring systems tracking parallel execution efficiency.

Because when you find a blockchain that actually solves the hard problems, you build for it.

P2P.org has been stress-testing Monad since Testnet-1. We're accepting institutional staking partnerships for mainnet launch.

If you're performance hungry but value risk-averse infrastructure, reach out to our team: https://link.p2p.org/bdteam