Why Sui's Object-Centric Model Is Uniquely Suited for Scalable CLOBs

EVM state is a global bottleneck. Every orderbook update must modify a single, shared storage slot, creating a sequential processing queue. This design caps throughput and inflates gas costs during high activity, as seen with dYdX's migration from StarkEx to Cosmos.

Sui's parallelized objects enable horizontal scaling. Each order is an independent, owned object. Matching engines can process thousands of orders concurrently without contention, a principle similar to Aptos' Block-STM but with finer-grained ownership semantics.

The bottleneck shifts from consensus to computation. In Sui, consensus finalizes dependent transactions, but independent order placements and cancellations are processed in parallel. This model mirrors how Solana's Sealevel parallelizes non-conflicting transactions, but with stronger safety guarantees for owned assets.

Evidence: Narwhal-Bullshark TPS vs. EVM Sequencers. Sui's Narwhal mempool and Bullshark consensus have demonstrated over 120,000 TPS in controlled environments for simple payments. For orderbooks, the parallelizable workload translates to sustained high throughput where EVM-based CLOBs like those on Arbitrum or Avalanche hit sequential state-update walls.

Ethereum's shared global state creates a single point of contention for all transactions. A CLOB's order matching engine must serialize operations on a single orderbook object, creating a performance ceiling. This is why high-throughput DEXs like dYdX migrated to a Cosmos appchain.

Sui's independent objects are owned assets with unique IDs. A limit order is a distinct object owned by the user, not a row in a shared ledger. This allows concurrent execution of unrelated orders, bypassing the serialization bottleneck entirely.

Parallel execution is deterministic because transactions touching independent objects have no conflicts. This is the core innovation of the Move language and Sui's runtime, enabling horizontal scaling similar to Solana's Sealevel but with stronger safety guarantees for complex assets.

Evidence: In a benchmark simulating a CLOB, Sui processed 297,000 order placements and cancellations per second. This is orders of magnitude above the ~50 TPS limit of a CLOB deployed naively on Ethereum's EVM.

Independent object ownership eliminates lock contention. In Ethereum's account model, a central order book contract creates a global bottleneck. On Sui, each limit order is a distinct, owned object, allowing transactions on unrelated orders to process in parallel without coordination.

The Move language's type system guarantees safe concurrency. Move's linear types and ownership semantics prevent data races at the language level, enabling the runtime to safely schedule thousands of order operations concurrently without complex developer logic.

Contrast with Solana's parallel execution. Solana's scheduler must analyze transaction dependencies at runtime. Sui's model makes dependencies explicit at the object level, reducing scheduling overhead and enabling more predictable, low-latency execution for high-frequency trading.

Evidence: DeepBook's throughput. Sui's native CLOB, DeepBook, demonstrates this by processing thousands of orders per second in a single block, a feat impossible on sequential EVM chains like Arbitrum or Optimism without significant centralization trade-offs.

Objects are native orderbooks. Every limit order is a programmable, ownable object. This eliminates the need for a monolithic, sequential contract state that bottlenecks Solana or Aptos, enabling parallel execution of independent trades.

Composability is a first-class property. Objects like orders or positions are directly referenceable assets. This enables on-chain order routing and cross-protocol composability without the proxy-wrapper overhead seen in EVM-based CLOBs like Hyperliquid or Aevo.

Ownership defines parallelism. The Move language's ownership types allow the runtime to prove non-overlapping order objects. This guarantees safe parallel settlement, a fundamental scaling advantage over optimistic concurrency models.

Evidence: 297k TPS benchmark. Sui's testnet demonstrated this throughput for simple payments. For CLOB operations, this translates to sub-second finality for millions of order matches, a requirement for institutional-grade exchange infrastructure.