Why Parallelizable VMs Are the True Scalability Breakthrough

Sequential VMs like the EVM serialize all transactions, creating a gas auction for every swap and liquidation. This makes sophisticated strategies economically unviable.

• Uniswap and Aave users pay for the latency of unrelated NFT mints.
• MEV bots exploit this, extracting $1B+ annually in arbitrage and liquidations.
• High-frequency DeFi and on-chain gaming are structurally impossible.

VMs track transaction dependencies at runtime, executing non-conflicting operations simultaneously. This is the true scalability breakthrough, not just higher TPS.

• Aptos' Block-STM achieves >100k TPS in optimistic parallel execution.
• Monad combines parallel EVM with pipelining and asynchronous I/O.
• Enables order-of-magnitude cost reduction for complex, composable transactions.

User-centric architectures separate declaration (intent) from execution. This creates massive, parallelizable batches of off-chain computation, demanding a VM that can settle them efficiently.

• UniswapX and CowSwap solve routing via off-chain solvers.
• Across uses a single optimistic verification for cross-chain intents.
• The VM's role shifts to high-throughput, atomic settlement of proven bundles.

The Problem: Traditional VMs treat all state as mutable, causing massive lock contention. The Solution: The Move language and Sui's object-centric model make data dependencies explicit. Transactions touching independent objects are processed simultaneously. This is the first-principles approach to parallel execution.

• Key Benefit: Linear scaling with added validators.
• Key Benefit: Eliminates gas wars for unrelated assets.

The Problem: EVM's single-threaded runtime serializes all transactions, wasting modern hardware. The Solution: Sealevel is a parallel smart contract runtime. It statically analyzes transactions before execution to schedule non-conflicting ones in parallel, akin to an optimistic concurrency control system.

• Key Benefit: Sustains ~2k-5k real TPS under load.
• Key Benefit: Enables atomic composability across parallel programs.

The Problem: The EVM is inherently sequential, but most transactions don't conflict. The Solution: Monad builds a fully parallel EVM-compatible L1. It uses MonadBFT (pipelined consensus), a parallel execution engine, and asynchronous I/O for state access. This is the maximalist path to scale the incumbent VM.

• Key Benefit: Targets 10,000+ TPS with single-slot finality.
• Key Benefit: Full bytecode compatibility with Ethereum tooling.

The Problem: Account-based models (EVM) have ambiguous state access, forcing serialization. The Solution: Fuel's UTXO-based model requires transactions to declare all state they will read/write upfront. This allows its parallel VM to execute massive batches of non-overlapping transactions deterministically.

• Key Benefit: Parallel validation from first transaction.
• Key Benefit: Ideal for high-throughput payments and DeFi orderflow.

The Problem: Predicting all state conflicts before execution (e.g., via access lists) is complex and limiting. The Solution: Block-STM uses Software Transactional Memory. It optimistically executes all transactions in parallel, detects conflicts, and re-executes only the conflicting ones. This is the runtime, optimistic approach.

• Key Benefit: Achieves parallel speedup on arbitrary, complex workloads.
• Key Benefit: Developer-friendly; no need to declare state access.

The Problem: L1 scaling is one path, but the Ethereum ecosystem demands scalable, compatible L2s. The Solution: Projects like Polygon zkEVM, zkSync, and Scroll are integrating parallel execution engines into their rollup sequencers. They batch parallelized execution proofs back to Ethereum.

• Key Benefit: Brings parallel throughput to the dominant EVM ecosystem.
• Key Benefit: Inherits Ethereum's security and liquidity ($50B+ TVL).

Parallelism fails when transactions compete for the same on-chain state. Hot assets like USDC or stETH create serialization points, capping theoretical gains. The real-world speedup is often far below the theoretical maximum.

• Amdahl's Law in Action: A chain with 5% contentious ops sees max ~20x speedup, not 100x.
• Real-World Impact: Solana validators already experience this, limiting TPS during memecoin frenzies.

Introducing non-deterministic, parallel execution makes smart contract behavior and debugging exponentially harder. This creates systemic risk and developer friction.

• Heisenbugs: Race conditions and phantom reads/writes are nearly impossible to reproduce.
• Audit Overhead: Security audits become more complex and costly, slowing innovation.
• EVM Incompatibility: Forces a hard break from the dominant Ethereum tooling ecosystem.

Maximizing parallel throughput requires high-end, specialized hardware, pushing validation towards centralized data centers. This undermines decentralization, the core value proposition of blockchains.

• Hardware Arms Race: Favors operators with high-core-count CPUs and GPUs.
• Geographic Concentration: Validators cluster in low-latency, high-power zones.
• Risk Profile: Creates a smaller, more homogenous set of failure points.

Parallel VMs decouple transaction fees from global block space contention, breaking the Ethereum-style fee market. This requires entirely new economic mechanisms for validator incentives and resource pricing.

• Unproven Models: New systems like Aptos' storage fees or Sui's object-centric pricing are experimental.
• MEV Complications: Parallel execution scrambles transaction ordering, creating new, unpredictable MEV landscapes.
• Subsidy Reliance: Many chains currently rely on token inflation to subsidize low fees.

Asynchronous execution between parallel chains or rollups creates massive complexity for cross-chain communication. Bridges and LayerZero, Axelar become more critical but also more vulnerable.

• Finality Lag: A parallel chain's instant finality is meaningless if the destination chain is slower.
• State Proof Nightmare: Proving the state of a highly concurrent system is computationally intensive.
• Fragmented Liquidity: Encourages walled gardens, counter to the Ethereum rollup-centric vision.

Developer and user migration from the Ethereum Virtual Machine (EVM) is slow. Network effects are powerful; a marginally better engine rarely displaces an entrenched standard. Solana and Aptos are still fighting this battle.

• Tooling Gap: Missing equivalents to Hardhat, Foundry, MetaMask.
• Talent Pool: Millions of devs know Solidity; thousands know Move or Rust for blockchain.
• Risk Aversion: Major protocols (Uniswap, AAVE) are slow to deploy on non-EVM chains.

EVM and other single-threaded VMs process transactions one-by-one, creating artificial congestion. This turns every DeFi interaction into a global race condition, capping TPS and inflating fees.

• Wasted Compute: Idle CPU cores while one transaction validates a signature.
• Congestion Tax: Network usage fees dominate over actual compute cost.
• Throughput Ceiling: Hard limit at ~100-200 TPS regardless of hardware.

VMs pre-analyze transactions to identify independent state access (read/write sets), executing non-conflicting ops simultaneously. This is the web-scale compute model applied to consensus.

• Linear Scaling: Add cores, get proportional TPS. 10k-100k+ TPS is achievable.
• Real Cost Pricing: Fees reflect compute/storage, not priority gas auctions.
• Developer Shift: Requires explicit state dependencies, a la Move's &mut or Fuel's inputs/outputs.

Parallel execution's hard problem isn't speed—it's resolving conflicts. Different VMs use distinct models, forcing a design choice.

• Optimistic (Solana): Execute all in parallel, then roll back conflicts. Needs fast hardware & low conflict rate.
• Conservative (Aptos Move): Pre-schedule using declared dependencies. Higher overhead, guaranteed success.
• UTXO Model (Fuel): Native parallelism per transaction input. Simple but requires explicit state design.

Parallel VMs enable application architectures previously impossible on-chain, moving beyond simple token swaps.

• On-Chain CEXs: Order book matching with sub-second latency and zero gas per trade.
• Fully On-Chain Games: Real-time physics and AI agents interacting concurrently.
• Massive DeFi Compositions: Hundreds of interdependent positions settled in one block without congestion fear.

Maximal throughput is useless if assets are locked in a silo. Bridging parallel chains requires new cross-chain messaging paradigms.

• Shared Security Hub: Use a parallel VM chain (e.g., Monad) as a settlement layer for rollups.
• Intent-Based Flows: Protocols like UniswapX and CowSwap can route orders to the fastest, cheapest parallel executor.
• Standardization Race: Will EVM adopt parallel extensions, or will Move become the new multi-chain standard?

The real metric is economic throughput per unit cost. Parallel VMs collapse the cost of state transitions, making micro-transactions and complex logic economically viable.

• Endgame: Block space becomes a commodity, not a scarce auction. $0.001 fees for any action.
• Shift: Value accrual moves from L1 block producers (seigniorage) to dApp developers and users (utility).
• Adoption Timeline: 2-3 years for developer mindshare shift; 5 years for dominant TVL.