Consensus Layer
The Consensus Layer leverages a hybrid Proof of Intelligence (PoI) and Proof of Space (PoSp) model integrated with Substrate's BABE+GRANDPA consensus framework to achieve ledger agreement. In theoretical test environments with minimal latency and co-located validators, it can process up to 1,000 transactions per second (txs/s).
TPS Estimates
This figure is derived using the formula TPS = 1 / (BlockTime + FinalizationTime), where BlockTime is set to 6 seconds (typical BABE block production) and FinalizationTime is approximately 1-2 seconds for GRANDPA finality, reflecting optimal conditions.
However, in real-world production environments—considering factors such as network latency (e.g., 50ms round-trip time), geographic validator distribution, and ZKP verification overhead—throughput is expected to range between 100-500 txs/s. This adjusted estimate is based on empirical Substrate performance data, accounting for additional computational costs introduced by ZKP verification and custom pallet execution
Note: The 1,000 txs/s figure assumes negligible network latency and perfect validator synchronization; actual performance may be lower due to network delays, validator downtime, or increased transaction complexity, as reflected in the more conservative 100-500 txs/s range. It is important to distinguish between regular transaction throughput and ZK-verified AI computations. While our base layer can achieve 100-500 TPS for simple transactions, ZK-verified AI computations face computational constraints that limit their throughput relative to standard transactions. The inherent complexity of zero-knowledge verification for sophisticated AI workloads introduces verification overhead that must be carefully managed within the blockchain's throughput capacity.
We are actively exploring techniques to improve this throughput, including batch verification methods that amortize verification costs across multiple similar computations, and recursive proof systems that enable proof composition without linear verification scaling. These approaches represent promising directions for addressing the inherent tension between high-throughput consensus and computationally intensive ZK verification.
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