_To bridge the gap between theoretical maximums and real-world performance, ongoing work focuses on:_ - [[ZKP/Research/Privacy Pools for Scalability/Recursive Proofs for Batching|Recursive SNARKs:]] Enabling proof aggregation to reduce on-chain costs and improve scalability [47, 49] - **Parallel Proof Generation:** Distributing proof computation across nodes to minimize T_p for AI tasks - [[zk-Rollup Architecture|Parachain Scaling]]: Leveraging Polkadot's parachain architecture for horizontal scaling and specialized AI computation chains These enhancements aim to ensure the ZKP ecosystem delivers robust performance for decentralized AI applications in production settings. _The performance metrics presented represent theoretical targets based on component benchmarks. In future testnet deployments, we expect to gather more realistic performance data reflecting:_ - _Transaction throughput variability based on network conditions - [[ZKP/ZKP Base Layer/ZKP Blockchain/Storage Layer/Network Security Under Load/Network Security of Storage Layer|Storage retrieval latency]] distributions across different network topologies - _ZKP verification costs at various batch sizes - System resilience under simulated attack conditions _These future measurements will provide a more conservative basis for application development on the platform, and we expect real-world performance to differ from theoretical maximums described here._ See also: [[ZKP/ZKP Base Layer/ZKP Blockchain/Technical Build Consensus Layer/Consensus Model|Consensus Model]]