The Future of State Management Is Parallel

EVM chains process transactions one-by-one, creating artificial congestion and high fees. This limits throughput to ~15-30 TPS on Ethereum L1, capping the entire ecosystem's economic potential.

• Resource Waste: Idle CPU cores while network is congested.
• Fee Volatility: Users bid for single-file slot access.
• Developer Friction: DApps cannot scale independently.

Aptos' Block-STM uses software transactional memory to speculatively execute transactions in parallel, then validate. Sui's model treats independent assets as distinct objects, enabling parallel settlement.

• Optimistic Parallelism: Achieves ~160k TPS in ideal lab conditions.
• Deterministic Finality: Maintains atomic composability for dependent transactions.
• Horizontal Scaling: Throughput increases with more cores, not higher clock speed.

Solana's Sealevel runtime pre-declares state access, allowing simultaneous non-conflicting transactions. Monad is building a parallel EVM with asynchronous execution and a custom state database.

• Explicit Parallelism: Requires upfront state declaration from programs.
• Pipelined Validation: Separates transaction processing into parallel stages.
• EVM Compatibility: Monad's approach promises 10,000+ TPS for existing smart contracts.

Maximal parallelism requires sacrificing instant, atomic composability across the entire state. This forces a new architectural paradigm for DeFi and applications.

• Sharded State: Like Celestia's data availability, execution can be partitioned.
• Intent-Based Flow: Systems like UniswapX and CowSwap separate routing from settlement.
• New Primitives: Developers must design for localized state access and asynchronous results.

The stack must evolve. RPC providers like Alchemy and QuickNode are optimizing for parallel query patterns. Indexers like The Graph must handle concurrent state reads.

• Query Parallelization: Serving 10,000+ RPS requires distributed query engines.
• State Snapshot Isolation: Providing consistent views for parallel execution engines.
• Fee Estimation Complexity: Predicting costs in a multi-threaded block space auction.

Every major L1 and L2 roadmap now includes parallel execution. The winner will be the chain that delivers EVM-equivalent security with 10-100x throughput and minimal developer friction.

• Ecosystem Lock-in: The chain with the best parallel tooling wins the next wave of apps.
• VC Bets: Monad, Sei, Aptos, Sui represent $1B+ in dedicated funding.
• Endgame: A multi-core world where blockchain throughput is limited by hardware, not consensus.

Sui's Move-based object model treats assets as first-class citizens with explicit ownership metadata, enabling the runtime to trivially identify non-overlapping transactions for parallel execution.\n- Key Benefit: Deterministic, static transaction analysis for ~100k TPS in optimal conditions.\n- Key Benefit: Eliminates global state contention, the root cause of congestion in EVM chains.

Aptos's Block-STM (Software Transactional Memory) is a parallel execution engine that processes transactions optimistically, detects conflicts, and re-executes only the necessary subset.\n- Key Benefit: Achieves ~160k TPS with 32 cores without requiring developers to reason about parallelism.\n- Key Benefit: Backward compatible; existing Solidity/Vyper contracts can be parallelized with minimal changes.

Monad is building a fully parallelized EVM L1 from the ground up, combining a parallel execution engine, deferred execution, and a custom state database (MonadDB).\n- Key Benefit: 10,000+ TPS target with 1-second block times and single-slot finality.\n- Key Benefit: Maintains full bytecode compatibility, allowing seamless migration of Ethereum tooling and dApps.

Sei V2 integrates a high-performance parallel EVM environment with Sei's existing optimistic block processing and native order-matching engine.\n- Key Benefit: Processes independent transactions in parallel while maintaining atomic composability for dependent ones.\n- Key Benefit: Leverages Ethereum tooling (Reth execution client) for immediate developer onboarding and a massive dApp ecosystem.

Fuel uses a strict UTXO model combined with pipelined transaction processing to achieve parallel validation and execution. Its virtual machine, FuelVM, is designed for this paradigm.\n- Key Benefit: UTXOs are inherently isolated, providing a mathematical guarantee of which transactions can be processed in parallel.\n- Key Benefit: Enables state minimization, reducing node hardware requirements and improving decentralization.

Ethereum's single-threaded EVM processes transactions one-by-one, creating artificial congestion and high fees. This limits throughput to ~15-30 TPS regardless of network capacity.\n- Root Cause: Global shared state requires sequential ordering to prevent non-determinism (e.g., double-spends).\n- Consequence: Blockspace becomes a scarce, auctioned commodity, pricing out all but the highest-value transactions.

Every transaction globally contends for a single CPU core. This creates massive inefficiency and caps throughput at ~10-100 TPS for most L1s. The result is predictable: network congestion, volatile fees, and a poor UX for any real application.

• Global Contention: One failed NFT mint can delay a billion-dollar DeFi swap.
• Wasted Resources: >95% of validator compute sits idle.
• Economic Ceiling: Limits the Total Addressable Market (TAM) for on-chain activity.

Architectures like Sui's Move and Aptos' Block-STM treat state as a set of independent objects. Transactions touching disjoint state are executed simultaneously, unlocking hardware-level parallelism. This is a fundamental rewrite of the consensus-to-execution stack.

• Deterministic Parallelism: Conflicts are detected and re-executed, not prevented.
• Sub-second Finality: Enables ~100k+ TPS theoretical throughput.
• Linear Cost Scaling: More users = more parallel paths = lower average cost.

Solana's Sealevel runtime proves parallel execution works at scale today. It uses explicit state declarations in transactions to schedule non-conflicting operations across all validator cores simultaneously. This is why it sustains ~3k-5k TPS with ~400ms block times.

• Hardware Saturation: Leverages all CPU cores and SSDs.
• Explicit Dependencies: Developer specifies state access, enabling optimal scheduling.
• Proven Scale: $4B+ TVL and leading DEX volumes demonstrate production readiness.

The EVM's global state is inherently serial. Projects like Monad (parallel EVM L1) and Polygon (parallel execution for PoS) are deploying speculative execution and optimized state access to retrofit parallelism. This is the critical upgrade path for the $50B+ EVM ecosystem.

• Speculative Execution: Guess dependencies, revert if wrong—net win.
• State Access Optimization: Minimize read/write conflicts.
• Backwards Compatibility: Full EVM bytecode support is non-negotiable.

Sharding (e.g., Ethereum Danksharding, Near) splits data and consensus but often leaves execution serial within a shard. The endgame is parallel execution + sharding. This combines intra-shard parallelism (handling complex apps) with inter-shard scalability (handling user load).

• Two-Dimensional Scaling: Parallelism (vertical) + Sharding (horizontal).
• Complex App Support: A single shard must run a high-throughput DEX.
• Composability Preservation: Limits the fragmentation inherent to pure sharding.

Serial chains will be relegated to niche use cases. For architects, the choice is clear: either migrate to a parallel chain (Sui, Aptos, Solana, Monad) or lead the parallelization of your own stack. The performance differential will be >100x within 24 months, making current architectures economically non-viable for mass-market apps.

• Performance Arbitrage: Users and liquidity flow to the best engine.
• New Design Space: Enables previously impossible applications (e.g., CEX-speed DEXs, on-chain gaming).
• Mandatory Upgrade: This is not an optimization; it's a requirement for survival.