Every node repeats work. The core design of blockchains like Ethereum and Solana requires each validator to execute every transaction to verify state transitions. This redundancy is the fundamental tax on execution performance, capping throughput at the speed of a single node.
the-modular-blockchain-thesis-explained
Blog
The Cost of Consensus Overhead on Execution Performance
Monolithic architectures like Ethereum and Solana force validators to redundantly compute every transaction, creating a hard scalability ceiling. Modular blockchains decouple consensus from execution, enabling specialized layers like Arbitrum and Optimism to scale independently. This is the core thesis of the modular stack.
introduction
THE OVERHEAD
The Redundant Computation Tax
Blockchain consensus forces every node to repeat the same work, creating a massive performance bottleneck.
Parallel execution is a patch. Optimistic and zk-rollups like Arbitrum and zkSync mitigate this by moving execution off-chain. However, they still rely on a centralized sequencer or prover, reintroducing a single-point-of-computation bottleneck at the L2 layer.
The metric is single-threaded speed. The theoretical maximum TPS for a blockchain is the single-threaded execution speed of its slowest validating node. This is why Solana's 50k TPS claim is contingent on validators running high-end hardware, creating centralization pressure.
Evidence: The Solana validator spec. To keep up with the network, a Solana validator requires a CPU with a 5.2 GHz+ clock speed. This requirement proves the system's performance is gated by the sequential processing power of individual machines, not aggregate network capacity.
key-trends
THE COST OF CONSENSUS OVERHEAD
The Modular Shift: Three Data-Backed Trends
Monolithic chains bundle consensus, execution, and data availability, forcing every node to process every transaction. This is the root of the scalability trilemma.
01
The Problem: The Monolithic Bottleneck
In monolithic designs like Ethereum L1, every validator must execute every transaction to reach consensus, capping throughput at the speed of the slowest node. This creates a direct trade-off between decentralization and performance.\n- Throughput Ceiling: Ethereum L1 maxes at ~15-30 TPS, while Solana's monolithic high-performance approach pushes ~5,000 TPS but with centralization trade-offs.\n- Resource Contention: Execution, settlement, and consensus compete for the same block space, leading to volatile, auction-based gas fees.
15-30 TPS
Ethereum L1 Cap
~5k TPS
Monolithic Max (Solana)
02
The Solution: Decoupling Consensus from Execution
Modular architectures like Celestia, EigenLayer, and Avail provide a neutral data availability (DA) and consensus layer. Rollups (e.g., Arbitrum, Optimism, zkSync) post compressed transaction data here and execute independently.\n- Parallel Scaling: Execution layers (rollups) process transactions in parallel, unconstrained by a global state. Theoretical throughput scales with the number of rollups.\n- Cost Efficiency: By only posting data fingerprints to the base layer, rollups reduce L1 gas consumption by 10-100x for users.
10-100x
Gas Reduction
Parallel
Execution Scaling
03
The Trend: Specialized Execution Environments
The modular stack enables purpose-built execution layers optimized for specific use cases, moving beyond the one-size-fits-all virtual machine.\n- App-Specific Rollups: dYdX (trading), Immutable (gaming), and Lyra (options) run their own chains for maximal control and performance.\n- VM Innovation: Fuel uses parallel UTXO execution, Eclipse allows any VM (SVM, Move) to run on any DA layer, and Arbitrum Stylus enables Rust/C++ smart contracts.
App-Specific
Optimization
Multi-VM
Future
deep-dive
THE BOTTLENECK
Anatomy of Inefficiency: Why Monolithic Consensus Fails at Scale
Monolithic blockchains force every node to process every transaction, creating a fundamental performance ceiling.
Consensus is the bottleneck. In a monolithic architecture, the consensus layer dictates the speed of the entire system. Every validator must re-execute every transaction to verify state transitions, making sequential execution the limiting factor for throughput.
Resource contention is inevitable. The same node set competes for CPU, memory, and bandwidth to handle consensus, execution, and data availability. This creates a scalability trilemma where improving one dimension degrades another, as seen in Solana's trade-offs between speed and hardware requirements.
Vertical scaling hits a wall. Increasing block size or lowering block time to boost throughput exponentially increases the state bloat and hardware burden on validators. This centralizes the network, undermining the security model it was designed to protect.
Evidence: Ethereum's base layer processes ~15-30 TPS. Even high-performance chains like Solana, which pushes for 50k+ TPS, require specialized, expensive hardware for validators, demonstrating the physical limits of the monolithic model.
CONSENSUS OVERHEAD
The Performance Tax: Monolithic vs. Modular Execution
Quantifying the latency and throughput penalty paid by execution environments for achieving consensus finality.
| Performance Metric | Monolithic L1 (e.g., Ethereum, Solana) | Modular L2 (e.g., Arbitrum, Optimism) | Modular L2 with Enshrined Sequencing (e.g., zkSync, Starknet) |
|---|---|---|---|
Consensus Finality Latency | 12-15 seconds | ~1-2 seconds (to L1) | ~1-2 seconds (to L1) |
Sequencing Latency (Inclusion) | 0 seconds (native) | ~1-12 seconds (to DA layer) | 0 seconds (native) |
Max Theoretical TPS (Execution Only) | ~100,000 (Solana) | ~10,000+ | ~10,000+ |
Effective TPS (After Consensus) | ~5,000 (Solana) | ~1,000-2,000 | ~1,000-2,000 |
State Growth Cost | High (on-chain) | Low (off-chain, posted to DA) | Low (off-chain, posted to DA) |
MEV Capture Point | Validator | Sequencer | Sequencer (potentially protocol-owned) |
Cross-Domain Sync Complexity | None (single domain) | High (fraud/zk proofs, bridging latency) | High (validity proofs, bridging latency) |
Client Diversity Overhead | High (full node sync) | Low (verification of proofs only) | Low (verification of proofs only) |
counter-argument
THE PERFORMANCE TRADEOFF
The Monolithic Rebuttal: Security Through Redundancy
Monolithic architectures sacrifice raw execution speed to consolidate security and state, eliminating the systemic risk of cross-chain bridges.
Consensus is the bottleneck. Every transaction in a monolithic chain like Solana or Ethereum must be ordered and validated by the entire network, creating an inherent latency and throughput ceiling that modular execution layers bypass.
Security demands redundancy. This overhead is the cost of a single security model. Modular chains like Celestia or Avail outsource data availability, but reintroduce bridge risk—the very problem monolithic designs solve.
Execution is not the constraint. The real-world bottleneck is state growth and access, not CPU. Monolithic chains optimize for this with parallel execution (Solana's Sealevel, Monad's MonadDB) and state expiry (Ethereum's Verkle trees).
Evidence: Ethereum's rollup-centric roadmap is a concession. The base layer's ~15 TPS forces execution off-chain, creating a fragmented ecosystem reliant on bridges like Across and LayerZero, which become central points of failure.
protocol-spotlight
THE CONSENSUS BOTTLENECK
Architecting the Modular Stack: Key Infrastructure
Monolithic blockchains force execution to wait on global consensus, creating a fundamental performance tax. Modular designs separate these concerns.
01
The Monolithic Tax: Every Node Recomputes Everything
In Ethereum or Solana, every validator must execute every transaction to verify state. This creates a hard ceiling on throughput and inflates hardware requirements.\n- Wasted Compute: >99% of nodes' execution work is redundant verification.\n- Throughput Ceiling: Capped by the slowest node in the network, not aggregate capacity.
15-50
TPS Limit
>99%
Redundant Work
02
Rollups: Delegate Execution, Keep Consensus
L2s like Arbitrum and Optimism move execution off-chain, posting compressed proofs or data back to Ethereum. Consensus (settlement & data availability) remains on L1.\n- Scalability: Achieves ~1000-4000 TPS by batching transactions.\n- Security Inheritance: Leverages Ethereum's validator set for ultimate security.
1000-4000
Effective TPS
10-100x
Cheaper Gas
03
Celestia & Data Availability Layers
Decouples data availability (DA) from consensus and execution. Rollups post transaction data to a specialized DA layer like Celestia or EigenDA, drastically reducing costs.\n- Cost Reduction: DA costs are ~99% lower than Ethereum calldata.\n- Modular Freedom: Enables rollups to mix-and-match execution, settlement, and DA layers.
~99%
Lower DA Cost
$0.001
Per MB (est.)
04
The Validium/Sovereign Rollup Trade-Off
Takes modularity further by moving both execution and data availability off the base layer. zkSync's Validium or dYmension's RollApps offer ultra-low fees but introduce new trust assumptions.\n- Performance: Throughput can exceed 10,000 TPS.\n- Security Model: Relies on the DA layer's honesty or a committee, a trade-off for cost.
>10k
Potential TPS
$0.01
Avg. Tx Cost
05
Parallel Execution Engines
Even within an execution layer, monolithic scheduling is a bottleneck. Sui, Aptos, and Monad use parallel execution to utilize multi-core hardware, bypassing sequential constraints.\n- Hardware Efficiency: Leverages modern multi-core CPUs instead of single-threaded limits.\n- Throughput Multiplier: Non-conflicting transactions process simultaneously.
10-100x
Throughput Gain
~100ms
Finality
06
The Endgame: Specialized Consensus (Narwhal, Bullshark)
Consensus itself can be modularized. Sui's Narwhal (DAG-based mempool) separates data dissemination from consensus logic (Bullshark). This allows execution to proceed before consensus finalizes.\n- Pipelining: Decouples network and consensus latency.\n- Foundation: Enables the high throughput of parallel execution engines.
160k+
TPS (Theoretical)
Sub-second
Latency
takeaways
THE EXECUTION BOTTLENECK
TL;DR: The Consensus Overhead Thesis
Blockchain performance is gated by the cost of global consensus, forcing a trade-off between security, decentralization, and speed.
01
The Monolithic Bottleneck
Traditional L1s like Ethereum and Solana force every node to process every transaction, creating a hard ceiling on throughput. This consensus overhead is the root of high fees and network congestion during peak demand.
- Sequencing & Execution are fused with consensus.
- State growth burdens all validators equally.
- Limits practical TPS to ~10-50k even with optimizations.
~15 TPS
Ethereum Base
1000x
Validator Load
02
Modular Separation: Celestia & EigenLayer
Decouples consensus (settlement/security) from execution, allowing specialized layers to scale independently. Data availability layers like Celestia provide cheap consensus for blob data, while restaking protocols like EigenLayer provide cryptoeconomic security for new chains.
- Execution layers (Rollups) offload computation.
- Consensus becomes a commodity service.
- Enables massively parallel execution environments.
$0.001
Per MB DA Cost
100+
Parallel Chains
03
Parallel EVMs: Monad, Sei, Aptos
Attacks the problem within a monolithic architecture by using parallel execution and advanced state access models. These chains use techniques like Monad's deferred execution and Aptos's Block-STM to circumvent the sequential processing limit of the EVM.
- Pipelining for consensus, execution, and storage.
- Optimistic parallelization with conflict resolution.
- Targets 10,000+ TPS for simple payments.
10,000+
Target TPS
-90%
Latency
04
The Validator Overhead Tax
The requirement for full nodes to verify all transactions imposes a massive hardware and operational cost, centralizing node operation. This is the real cost of consensus overhead, measured in $ billions in staked capital and exorbitant cloud compute bills.
- Hardware costs scale with chain usage.
- Creates minimum viable stake barriers.
- Leads to infrastructure oligopolies (e.g., AWS).
32 ETH
Min Stake
$1M+
Annual Node Cost
05
Solana's Throughput Gambit
Embraces extreme hardware requirements and optimized consensus (Turbine, Gulf Stream) to minimize overhead, betting on Moore's Law. It represents the peak monolithic approach, pushing the limits of what a single global state machine can do.
- Leader-based transaction forwarding.
- Localized Fee Markets via priority fees.
- Achieves ~3k-5k real TPS but requires 128GB+ RAM validators.
~4k TPS
Sustained
128GB
Min RAM
06
The Endgame: Sovereign Rollups & Appchains
The logical conclusion: every high-performance application becomes its own execution environment with shared security. Fuel, Dymension RollApps, and Polygon CDK chains exemplify this. Consensus overhead is paid once by the settlement layer and amortized across thousands of app-specific chains.
- Maximizes execution flexibility (non-EVM VMs).
- Isolated failure domains and gas markets.
- Ultimate vertical scaling for dApps.
Unlimited
Theoretical TPS
~100ms
Appchain Latency
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