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zk-rollups-the-endgame-for-scaling
Blog

Why Monolithic L1s Are Winning the Real-World Throughput Race

A first-principles analysis of why Solana's monolithic architecture delivers superior, usable throughput today, while the modular rollup stack introduces fragmentation and latency that cripples real-world performance.

introduction
THE THROUGHPUT REALITY

Introduction

Monolithic L1s are achieving real-world transaction throughput that modular architectures cannot yet match.

Monolithic architectures win on latency. A single, optimized execution environment avoids the consensus and data availability overhead of cross-chain communication seen in modular stacks like Celestia or EigenDA.

The bottleneck is state access. Solana's parallel execution via Sealevel and Sui's object-centric model minimize contention, while Ethereum's rollups like Arbitrum and Optimism serialize execution through a single sequencer.

Evidence: Solana consistently processes over 2,000 TPS with sub-second finality; Ethereum's entire rollup ecosystem combined processes under 50 TPS with multi-minute delays.

thesis-statement
THE DATA

The Core Argument: Latency is the Real Bottleneck

Monolithic L1s like Solana and Sui achieve superior real-world throughput by eliminating cross-domain latency, a fundamental constraint for modular stacks.

Latency defines real throughput. Throughput is not just raw compute (TPS) but the time for finality. A user's transaction completes when its state is final. Cross-chain latency from modular designs adds seconds or minutes, collapsing effective throughput for real applications.

Monolithic execution is synchronous. Chains like Solana process transactions within a single, globally ordered state machine. This atomic composability allows DeFi protocols like Jupiter and Raydium to execute complex swaps in one block, a physical impossibility across separate rollups or L2s.

Modular stacks serialize latency. A user bridging from Arbitrum to Optimism via a canonical bridge or a liquidity network like Across experiences multiple finality delays. This serialized latency makes high-frequency trading and complex cross-L2 DeFi strategies non-viable at scale.

Evidence: End-to-End Finality. Solana achieves sub-2 second finality for all transactions. An equivalent cross-rollup swap on Ethereum, using Hop or Stargate, requires waiting for L2 finality, L1 bridge confirmation, and destination L2 inclusion, taking minutes. The monolithic chain wins the real-world race.

MONOLITHIC VS. MODULAR

Throughput & Latency Benchmarks: Theory vs. Reality

Comparing the real-world performance of leading monolithic L1s against the theoretical ceilings and practical bottlenecks of modular architectures.

Metric / CharacteristicSolana (Monolithic)Sui (Monolithic)Modular Stack (Celestia + Arbitrum)

Peak Theoretical TPS

65,000

297,000

1,000+

Sustained Real-World TPS (30d avg)

4,500

850

40

Time to Finality

< 2 sec

< 1 sec

~20 min (to L1)

Latency for User Tx (P95)

< 10 sec

< 5 sec

~12 sec (L2) + ~20 min (L1)

State Growth Burden

Validator (High Cost)

Validator (High Cost)

DA Layer + Sequencer (Decoupled)

Max Blockspace per Second

~80 MB

~184 MB

~16 MB (Celestia Blob)

Cross-Shard/Chain Atomic Composability

Protocol Revenue (Annualized)

$350M

$120M

< $10M

deep-dive
THE LATENCY COST

The Modular Overhead Tax: Fragmentation, Proving, and Bridging

Modular architectures sacrifice end-to-end latency for scalability, creating a fundamental overhead that monolithic L1s avoid.

Modular architectures fragment state. Separating execution, settlement, and data availability creates a coordination problem that monolithic chains solve internally. This introduces a latency tax for every cross-domain operation.

Zero-knowledge proofs are not free. The proving time for validity proofs on chains like Polygon zkEVM or Starknet adds a 10-20 minute finality delay. This is a hard constraint that monolithic L1s like Solana or Sui do not have.

Bridging is the new bottleneck. Users and applications must now trust and pay for bridges like LayerZero or Across, adding cost, complexity, and security risk. This is a direct tax on composability that monolithic environments eliminate.

Evidence: A simple swap on a rollup like Arbitrum involves L1 bridging latency, L2 proving time, and potential CEX withdrawal delays. A monolithic chain like Solana executes and settles the same swap in under 400ms, end-to-end.

counter-argument
THE THROUGHPUT REALITY

Steelman: The Modular Long-Game and Data Availability

Monolithic L1s currently dominate real-world transaction throughput by eliminating the consensus and data availability bottlenecks inherent to modular designs.

Monolithic architectures optimize for locality. A single, tightly integrated execution, consensus, and data availability layer minimizes cross-domain latency and coordination overhead. This design enables Sui and Aptos to achieve peak throughputs exceeding 100k TPS in controlled benchmarks, a figure modular stacks struggle to match in production.

Modular data availability is a tax. Every rollup transaction must post its data to a separate DA layer like Celestia or EigenDA, incurring fixed latency and cost. This creates a throughput ceiling determined by the DA layer's own consensus speed, a bottleneck monolithic chains avoid entirely.

Real-world adoption demands finality speed. Applications like high-frequency trading or gaming require sub-second finality. The sequential trust model of modular stacks (L2 finality -> DA finality -> L1 finality) adds unavoidable delays that monolithic L1s like Solana circumvent with a single, rapid consensus step.

Evidence: The Solana network has consistently processed over 3,000 TPS of real user transactions for sustained periods, a figure that exceeds the combined sustained TPS of all major Ethereum L2s (Arbitrum, Optimism, Base) by an order of magnitude, demonstrating the monolithic advantage.

takeaways
WHY MONOLITHIC L1S ARE WINNING

Key Takeaways for Builders and Investors

The modular vs. monolithic debate is over for high-throughput, real-world applications. Here's the data-driven case for unified execution layers.

01

The Latency Tax of Modular Stacks

Cross-domain messaging between separate execution, settlement, and data layers introduces unavoidable latency and complexity overhead. This kills user experience for applications requiring fast, atomic composability.

  • ~2-10 second finality for optimistic bridges vs. sub-second on monolithic L1s.
  • Sequencer pre-confirmations (like Arbitrum) are a band-aid, not a solution, adding trust assumptions.
  • Apps like high-frequency DEXs and on-chain games cannot tolerate this tax.
2-10s
Bridge Latency
<1s
Monolithic Finality
02

Solana's Throughput Is a Feature, Not a Bug

Solana's monolithic architecture with localized fee markets and a single global state enables sustained ~3k-5k TPS with ~400ms block times. This is the benchmark for real-world scaling.

  • Sealevel parallel VM processes non-conflicting transactions simultaneously.
  • No fragmented liquidity; all assets and protocols share the same state and security.
  • The cost of synchronous composability (e.g., a single arbitrage transaction across 10 DEXs) is ~1000x cheaper than on a modular rollup stack.
3k-5k
Sustained TPS
400ms
Block Time
03

The Sovereign Rollup Fallacy for Apps

Sovereign rollups (e.g., Celestia, Eclipse) offer theoretical sovereignty but impose operational burden on applications to bootstrap validators, sequencers, and liquidity. This is a non-starter for most teams.

  • You are building an entire chain, not deploying a contract.
  • Zero shared security with the parent chain; you must secure your own validator set.
  • Fragmented liquidity and disjointed user experience isolate your app from the main economic hub.
$0
Shared Security
100%
Ops Burden
04

Aptos & Sui: Move and Parallel Execution

Next-gen monolithic L1s like Aptos and Sui are betting on advanced VMs (Move) and aggressive parallelization to push throughput beyond 100k TPS. This is an architectural moat.

  • Move's resource-oriented model enables safer, more efficient on-chain assets.
  • BlockSTM (Aptos) and Narwhal-Bullshark (Sui) are parallel execution engines that dynamically find concurrency.
  • This creates a developer experience advantage where scaling is automatic, not a puzzle of layer-2 orchestration.
100k+
Theoretical TPS
Auto
Parallelism
05

The Liquidity Sinkhole of Fragmentation

Modularity fragments liquidity across dozens of rollups and L2s. Bridges like LayerZero and Axelar are bandwidth constrictors, not liquidity unifiers. This creates arbitrage opportunities but cripples capital efficiency for end-users.

  • TVL is trapped in silos; moving it is slow and expensive.
  • Yield farming across chains requires managing 5+ different wallets and gas tokens.
  • Monolithic L1s like Solana and Avalanche (with its subnets) keep liquidity in a single, deep pool.
5+
Wallets Needed
Siloed
Liquidity
06

Build Where the Users Are (Not Where It's Cheap)

The primary cost for a successful application is user acquisition and retention, not gas fees. Monolithic L1s with superior UX (speed, simplicity) win here. Ethereum L2s compete on cost, but monolithic L1s compete on experience.

  • Solana's Phantom wallet UX is a generation ahead of fragmented EVM meta-transaction hell.
  • ~$0.001 average transaction cost is cheap enough for 99% of use cases.
  • The market has voted: Solana's daily active addresses consistently 2-5x Ethereum's.
$0.001
Avg. TX Cost
2-5x
More Users
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