The Performance Mirage: When Theoretical TPS Meets Real-World Hardware

The network's advertised peak is a synthetic benchmark under perfect conditions. Real-world performance is gated by validator hardware diversity and state growth, causing frequent congestion and failed transactions during memecoin frenzies.

• Real TPS: ~3k-5k for user transactions under load.
• Bottleneck: Leader scheduling and Turbine's propagation to low-end validators.

Individual subnets can achieve high throughput, but cross-subnet communication via the Primary Network creates a coordination bottleneck. The P-Chain becomes a single point of contention for validator set management and cross-chain asset transfers.

• Theoretical Limit: Each subnet ~4.5k TPS.
• Systemic Limit: Primary Network consensus for cross-subnet ops.

Zero-knowledge proofs decouple execution from verification, but the prover is a centralized hardware bottleneck. Batch generation times create ~1-4 hour finality delays, making the user experience feel like optimistic rollups without the fraud proof window.

• Execution TPS: High.
• Finality TPS: Gated by prover capacity and cost.

The Move-based object model allows parallel execution of independent transactions. However, real-world applications like AMMs and lending markets create contention on shared objects (e.g., liquidity pools), causing most TXs to execute serially and capping gains.

• Peak Gain: 100k+ TPS for simple transfers.
• Realistic Gain: ~2-10x over serial blockchains for DeFi.

As an OP Stack rollup, Base's performance is dictated by a single sequencer operated by Coinbase. While it provides ~2s latency, it's a single point of failure and censorship. The planned decentralization to a shared sequencer set like Espresso will introduce consensus overhead, trading some speed for liveness.

• Current Latency: ~2s (centralized).
• Future Cost: Added latency for decentralized liveness.

Monad attempts to solve the EVM's inherent serial execution by adding parallel processing, asynchronous I/O, and a custom state database (MonadDB). The bet is that hardware-aware optimization can yield ~10k real TPS without breaking compatibility. The unproven risk is in synchronization overhead for complex, interdependent transactions.

• Target TPS: ~10,000 real.
• Key Innovation: Pipelined execution with deferred state commitment.

Theoretical TPS assumes perfect, uncongested nodes. In reality, state growth and I/O bottlenecks on consumer-grade hardware cause nodes to fall behind, breaking consensus.\n- Key Issue: A 10k TPS chain can't be synced on a $200/month VPS.\n- Real Metric: ~50-100 GB/day of state growth cripples archival nodes.

High throughput floods the peer-to-peer mempool. Without sophisticated gossip protocols, transactions get lost or reordered, killing DeFi arbitrage and front-running guarantees.\n- Key Issue: Uncontrolled gossip leads to network partitions and inconsistent views.\n- Real Metric: >100ms propagation delay makes any sub-second block time meaningless.

Rollups and L2s promise scale by pushing data off-chain. But if that data isn't provably available on-chain, the system reverts to a fragile multisig. This is the core innovation of Celestia and EigenDA.\n- Key Issue: Without DA, you're not a rollup; you're a sidechain.\n- Real Metric: ~$0.50 per MB is the current cost for robust, scalable DA.

Most high-TPS protocols assume weak synchrony—messages arrive within a known bound. Real-world networks have blackholes, latency spikes, and ISP throttling. This breaks liveness.\n- Key Issue: A few slow nodes can halt or fork the entire chain.\n- Real Metric: 99.9% synchronous uptime is a fantasy; plan for 95%.