Peak TPS is meaningless. Advertised throughput assumes perfect conditions—zero contention, optimal block space use, and infinite data capacity—that never exist in production. Real-world performance is dictated by the data availability (DA) bottleneck on the underlying L1.
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Why L2 Throughput Claims Are Mostly Theoretical
A technical breakdown of the real-world constraints—state contention, I/O bottlenecks, and network latency—that render advertised Layer 2 TPS figures misleading for architects and investors.
introduction
THE REALITY CHECK
Introduction
Layer 2 throughput metrics are marketing constructs that ignore the practical constraints of data availability and cross-chain coordination.
Sequencers are centralized chokepoints. The single sequencer model used by Arbitrum and Optimism creates a performance ceiling and a single point of failure. Decentralized sequencer sets, like those planned by Espresso Systems, introduce latency that erodes the theoretical speed advantage.
Cross-chain activity dominates gas. User transactions are not isolated; they trigger cascading interactions with protocols like Uniswap, Aave, and LayerZero. The effective throughput for a complete user action is the slowest link in this cross-contract, often cross-chain, dependency chain.
Evidence: Arbitrum One's advertised 40k TPS contrasts with its sustained real-world average of ~15 TPS, as its capacity is ultimately throttled by Ethereum calldata costs and sequencer batch submission intervals.
key-insights
THE REALITY OF L2 THROUGHPUT
Executive Summary
Layer 2s promise massive scalability, but real-world performance is bottlenecked by data availability costs, sequencer centralization, and the base layer itself.
01
The Data Availability Bottleneck
Throughput is limited by the cost and speed of posting data to Ethereum. A chain claiming 100k TPS is meaningless if posting that data to L1 costs $1M/hour or creates a 12-hour finality delay.\n- Real TPS is a function of blob gas limits and market price.\n- Starknet and zkSync compete for the same constrained blob space.
~0.1 MB/s
Blob Throughput
12+ hrs
Finality Delay
02
Sequencer Centralization = Single Point of Failure
Most L2s (Optimism, Arbitrum) run a single, centralized sequencer. This creates a theoretical throughput ceiling and reintroduces MEV and censorship risks the ecosystem aimed to solve.\n- Decentralized sequencer sets (e.g., Espresso, Astria) are nascent and add latency.\n- Real throughput collapses if the sole sequencer goes offline.
1
Active Sequencer
~2s
Forced TX Delay
03
The Shared Resource Contention Problem
L2s are not siloed performance engines; they are tenants on a congested L1. A surge on Base or a popular NFT mint on Arbitrum can spike gas costs for every rollup by competing for blob space and calldata.\n- Throughput is non-guaranteed and variable.\n- EIP-4844 (blobs) helped, but did not eliminate contention.
100x
Gas Cost Spike
Shared
Resource Pool
04
State Growth & Execution Overhead
High throughput rapidly expands the state size that nodes must maintain. Without proper state expiry (like Verkle trees), node hardware requirements balloon, recentralizing the network. Execution clients (Geth, Reth) become the bottleneck, not the L1.\n- Theoretical TPS ignores state bloat.\n- Real-world sync times can stretch to weeks.
1 TB+
State Size
Days
Sync Time
thesis-statement
THE BOTTLENECK
The Throughput Lie: It's a Systems Problem
Advertised L2 throughput is a theoretical maximum, not a practical guarantee, because it ignores the systemic constraints of the underlying data layer and sequencer architecture.
Theoretical TPS is meaningless. A rollup's peak transactions per second (TPS) is a function of its block gas limit and average transaction size. This calculation ignores the data availability (DA) bottleneck on Ethereum, where blob space is a finite, auctioned resource. High throughput demands high blob consumption, which becomes prohibitively expensive during network congestion.
Sequencers are single points of failure. The centralized sequencer model used by Arbitrum and Optimism creates a systemic throughput ceiling. Even if the chain logic supports 100k TPS, a single sequencer's hardware and network capacity determines the real-world limit. This architecture trades decentralization for temporary performance, creating a fragile scaling illusion.
Cross-chain activity compounds the problem. High-throughput L2s attract complex applications that rely on bridges like Across and Stargate. Each cross-chain message is an extra transaction that consumes the same scarce sequencer and DA resources, eroding the advertised throughput for core application logic. The system's slowest component dictates the effective speed.
L2 PERFORMANCE BENCHMARK
The Reality Gap: Advertised vs. Sustained Throughput
Comparing theoretical peak TPS claims against real-world, sustained performance under load, factoring in data availability and execution bottlenecks.
| Metric / Bottleneck | Arbitrum One | Optimism (OP Mainnet) | Base | zkSync Era |
|---|---|---|---|---|
Advertised Peak TPS (Theoretical) | 40,000 | 2,000 | Not Disclosed | 20,000 |
Observed Sustained TPS (7d avg, Dencun) | 45 | 18 | 32 | 12 |
Max TPS in 1hr Surge (Observed) | 210 | 95 | 150 | 55 |
Primary Bottleneck | Sequencer Execution | Data Availability (Blobs) | Data Availability (Blobs) | Prover Capacity |
Time to Finality (Avg, L1 Confirmation) | ~12 min | ~12 min | ~12 min | ~1 hour |
Handles Congestion Spikes Gracefully | ||||
Throughput Scales with Blob Usage |
deep-dive
THE REALITY CHECK
The Three Real-World Bottlenecks
Layer 2 throughput is constrained by off-chain infrastructure, not just on-chain gas limits.
Sequencer compute is the bottleneck. A sequencer's ability to process and compress transactions determines real TPS, not the optimistic or ZK proof. This creates a centralized scaling limit before data hits Ethereum.
Data availability costs dominate. Posting calldata to Ethereum L1 is the primary cost for rollups like Arbitrum and Optimism. High L1 gas prices directly throttle L2 economic throughput, regardless of theoretical capacity.
Prover hardware is non-trivial. ZK-rollups like zkSync and StarkNet require specialized, expensive hardware for proof generation. This creates a capital and operational barrier that limits finality speed and decentralization.
Evidence: During the 2021 bull run, Arbitrum and Optimism experienced congestion and fee spikes not from their own limits, but from the high cost of posting their batch data to a congested Ethereum L1.
case-study
WHY L2 THROUGHPUT CLAIMS ARE MOSTLY THEORETICAL
Case Studies in Constraint
Peak TPS numbers ignore the real-world bottlenecks that govern user experience and economic viability.
01
The Sequencer Bottleneck
Centralized sequencers are a single point of failure for ordering transactions, creating a hard cap on realized throughput regardless of chain capacity.\n- Sequencer Censorship: A single operator can reorder or delay your tx, negating decentralization promises.\n- Data Availability Reliance: Throughput is gated by the cost and speed of posting data to L1 (Ethereum).
1
Active Sequencer
~12s
Finality Delay
02
Shared State Contention (Arbitrum Stylus)
Even with parallel execution, all transactions compete for access to shared, hot smart contract state (e.g., a major DEX pool or NFT mint), creating congestion.\n- Worst-Case Serialization: A single popular contract forces parallel chains to process sequentially.\n- Real TPS <<< Peak TPS: Observed throughput during a memecoin frenzy is a fraction of lab-tested benchmarks.
~100 TPS
Real-World Load
>40k TPS
Theoretical Peak
03
The Data Availability Tax
L2s must pay to post transaction data to Ethereum for security. High throughput makes this the dominant cost, forcing trade-offs.\n- Blob Pricing Volatility: Throughput economics break when blobspace on Ethereum is congested.\n- Solution: Validiums: Chains like StarkEx sacrifice L1 data for lower cost, trading off Ethereum's security for scalability.
~90%
Cost is DA
$0.01-$1+
Variable Tx Cost
04
Interoperability Friction
High throughput is meaningless if assets are trapped. Bridging between L2s or to L1 introduces latency and cost that dominates the user experience.\n- 7-Day Challenges: Optimistic rollup withdrawal delays act as a massive liquidity lock.\n- Bridge Liquidity Fragmentation: Moving value requires waiting for external liquidity pools to rebalance.
7 Days
Standard Delay
0.3%+
Bridge Fee
05
Prover Capacity Limits (ZK-Rollups)
Generating validity proofs for large blocks is computationally intensive, creating a production bottleneck separate from network bandwidth.\n- Proof Generation Time: A fast chain must wait minutes for its proof to be generated before finality.\n- Centralized Provers: High-end hardware requirements lead to prover centralization, a nascent risk.
~10 mins
Proof Time
Few
Active Provers
06
Economic Sustainability
Sustaining high throughput requires a constant, high volume of fee-paying transactions. In bear markets, low activity threatens sequencer/prover revenue and security.\n- Sequencer Extractable Value (SEV): Reliance on MEV for revenue creates misaligned incentives.\n- Subsidy Reliance: Many 'low-fee' periods are artificially sustained by token emissions, not organic demand.
<$0.001
Subsidized Fee
$2M+
Daily Emissions
counter-argument
THE REALITY OF BOTTLENECKS
The Optimist's Rebuttal (And Why It's Wrong)
Theoretical L2 throughput is irrelevant when constrained by centralized sequencers and data availability costs.
Sequencer Centralization is the bottleneck. Optimistic and ZK rollups advertise high TPS, but a single sequencer processes all transactions. This creates a centralized performance ceiling identical to a traditional server, negating decentralization benefits.
Data availability costs dominate. Publishing transaction data to Ethereum (via calldata or blobs) is the primary expense. Throughput is a function of blob capacity and price, not L2 execution speed. High activity on one chain raises costs for all.
Cross-chain fragmentation destroys liquidity. High throughput on Arbitrum or Optimism is useless if assets are stranded. Moving value requires slow, expensive bridges like Across or Stargate, creating systemic latency that dwarfs any L2 speed gain.
Evidence: During the 2024 memecoin frenzy, Arbitrum's sequencer experienced multi-hour delays despite 'high throughput' claims, while Base's blob usage saturated Ethereum's data layer, spiking costs across the ecosystem.
FREQUENTLY ASKED QUESTIONS
FAQ: Throughput Realities for Builders
Common questions about why advertised L2 throughput is often theoretical and the practical bottlenecks builders face.
L2 throughput claims are theoretical maximums that ignore real-world bottlenecks like state growth and data availability costs. They assume perfect conditions—empty blocks, simple transactions—which never happen in production. In practice, Arbitrum and Optimism see real TPS limited by sequencer capacity and the cost of posting data to Ethereum.
takeaways
WHY L2 THROUGHPUT IS THEORETICAL
Key Takeaways for Architects & Investors
Advertised TPS figures are often meaningless without context on real-world constraints and trade-offs.
01
The Data Availability Bottleneck
Throughput is gated by the cost and speed of posting data to L1 (Ethereum).
- Celestia and EigenDA offer cheaper alternatives but introduce new trust assumptions.
- Without sufficient DA capacity, sequencers cannot produce blocks at advertised speeds.
- Real-world TPS is often <10% of theoretical max due to this constraint.
<10%
Real-World TPS
$0.01-$0.10
DA Cost/Tx (Est.)
02
Sequencer Centralization = Single Point of Failure
Most L2s use a single, centralized sequencer for speed, creating a systemic risk.
- This creates ~500ms latency for user transactions but is a censorship vector.
- Decentralized sequencer sets (e.g., Espresso, Astria) are nascent and add latency.
- Throughput claims assume this single sequencer never fails or gets congested.
1
Active Sequencer
~500ms
Latency
03
The State Growth & Execution Wall
Even with infinite DA, execution and state growth become the bottleneck.
- Arbitrum Nitro and zkSync must manage exponentially growing state databases.
- High TPS quickly leads to >1 TB state sizes, crippling node synchronization.
- Solutions like Verkle Trees and stateless clients are years from production.
>1 TB
State Size Risk
Years
Solution Timeline
04
Interoperability Tax on Throughput
Bridging assets and messaging between L2s (LayerZero, Hyperlane) consumes critical block space.
- A 10% surge in bridge transactions can directly reduce throughput for core app traffic.
- This creates a trade-off: higher interoperability often means lower effective TPS for users.
- Polygon zkEVM and Arbitrum must allocate capacity for these cross-chain proofs.
10%+
Block Space Tax
High
Trade-off
05
Fee Market Realities vs. Lab Conditions
Advertised TPS assumes users pay near-zero fees, which is economically unsustainable.
- In reality, a $0.50 fee surge on L1 causes a mempool flood to L2s, congesting them.
- Sequencers prioritize fee-paying transactions, breaking the 'always cheap' promise.
- Networks like Base and Optimism see volatile fee spikes during L1 congestion.
$0.50
L1 Fee Trigger
Volatile
L2 Fees
06
The Shared Sequencer Future (Espresso, Astria)
The solution is a decentralized, shared sequencing layer that batches for multiple rollups.
- Enables atomic cross-rollup composability, unlocking new app designs.
- Mitigates the centralization bottleneck but adds protocol complexity and latency.
- This is the next infrastructure battlefront, with EigenLayer restakers as potential operators.
Atomic
Cross-Rollup
Nascent
Stage
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