How to Architect a Rollup for Scalable AI Inference

An AI inference rollup is a specialized Layer 2 blockchain designed to offload and scale AI model execution from a base Layer 1 like Ethereum. Its primary goal is to provide cost-effective, fast, and verifiable inference for decentralized applications (dApps). The architecture must solve three key challenges: executing large models efficiently, generating cryptographic proofs of correct execution, and settling results trustlessly on the base chain. Unlike general-purpose rollups, AI rollups are optimized for the computational patterns of neural networks, often using zk-SNARKs or zk-STARKs for succinct verification.

The core system architecture consists of several critical components. The Sequencer orders and batches user inference requests into blocks. The Prover Network, which can be decentralized, executes the AI model (e.g., a Llama 3-70B parameter model) and generates a validity proof attesting to the correctness of the computation. The Verifier Contract, deployed on the base layer (L1), is a lightweight smart contract that checks the submitted proofs. Finally, a Data Availability (DA) layer, which could be the L1, a dedicated DA layer like Celestia, or EigenDA, ensures the input data and model parameters are available for reconstruction and fraud challenges if needed.

Designing the execution environment is crucial. You typically choose between a zkVM (zero-knowledge Virtual Machine) like RISC Zero or SP1 for general compute, or a custom zk-circuit for a specific model architecture. For maximum performance, developers often hand-craft circuits for operations like matrix multiplications and activation functions (ReLU, GELU). This allows for highly optimized proof generation. The system must define a standard format for inference requests, such as a JSON payload specifying the model hash, input tensor, and requested output format, which gets included in the rollup's transaction data.

The proving mechanism is the most complex part. For each batch of inferences, the prover generates a single proof that encompasses all model executions. This proof demonstrates that for given public inputs (model ID, input hashes) and public outputs (result hashes), there exist valid private witnesses (the actual model parameters and computations) that satisfy the circuit. Using recursive proof aggregation, like with Plonky2 or Halo2, can significantly reduce the on-chain verification cost. The final proof is posted to the L1 Verifier Contract, which updates the rollup's state root to reflect the new, proven inference results.

To integrate this into applications, developers need a client SDK. This SDK would handle: constructing and signing inference transactions, estimating fees, submitting transactions to the rollup's RPC endpoint, and querying for proven results. A typical workflow involves: dApp -> SDK -> Sequencer -> Prover Network -> L1 Verifier. For users and smart contracts on the base chain, the proven inference result is available as a verified state commitment, enabling trustless use in DeFi, gaming, or autonomous agents. The entire architecture shifts the heavy compute off-chain while maintaining cryptographic security guarantees on-chain.

A rollup is a Layer 2 scaling solution that executes transactions off-chain and posts compressed data to a base layer (L1) like Ethereum for finality. For AI inference, this model is powerful: the heavy computational load of running large language models (LLMs) or diffusion models occurs off-chain, while the L1 provides a secure, trust-minimized ledger for results and state updates. The key architectural decision is choosing between a ZK-Rollup (which uses validity proofs) and an Optimistic Rollup (which uses fraud proofs). For AI, ZK-Rollups are often preferred for their instant finality and inherent privacy, though generating proofs for complex AI workloads is an active research area.

The core technical stack involves several distinct layers. The Execution Environment is where AI models (e.g., PyTorch or TensorFlow graphs) run. This is typically a specialized virtual machine or prover-friendly runtime. The Sequencer orders inference requests and batches them. The Prover (in a ZK-Rollup) generates a cryptographic proof (a zk-SNARK or zk-STARK) that verifies the correctness of the inference output given the input and model parameters. Finally, the Verifier Contract, deployed on the L1, checks these proofs and updates the rollup's state root. Data availability is handled by posting the essential input/output data and proof to a Data Availability Layer like Ethereum calldata or a Celestia blob.

Designing for AI inference introduces unique challenges. Prover overhead is significant; generating a ZK proof for a single inference can be 100-1000x more computationally expensive than the inference itself. Architectures must optimize for prover efficiency, often using custom proof systems like zkML frameworks (e.g., EZKL, RISC Zero). Model privacy is another consideration: while the input and output may be public, the model weights themselves can be kept private and attested to by the proof. The economic model must account for the high cost of proof generation, requiring efficient fee markets and potentially subsidized sequencing for batch proving.

Key prerequisites for developers include familiarity with Ethereum development (Solidity, Foundry/Hardhat), a strong understanding of zero-knowledge proof concepts (circuits, R1CS, Plonk), and experience with AI/ML frameworks. You'll need to choose a proving stack: Circom with SnarkJS for circuit design, RISC Zero for general-purpose ZKVM-based proving, or a specialized zkML library. For the execution client, you might modify an existing rollup stack like Arbitrum Nitro or zkSync Era's framework, or build a custom node that integrates a proving co-processor.

A practical first step is to define the state transition function for your AI rollup. What constitutes state? It could be a mapping of model hashes to verifier addresses, a ledger of inference credits, or a registry of attested results. The function must be deterministic and provable. For example, a transition where a user submits input X, the sequencer processes it with model M, and produces output Y with proof π. The L1 verifier checks π against the public input (X, M_hash, Y). Implementing this flow requires tight integration between your off-chain prover service and on-chain verifier contract.

Finally, consider the data lifecycle. Raw inference data is voluminous. You must design a data compression and availability scheme. Only the minimal data required to reconstruct state and validate proofs needs to be posted on-chain. Techniques like data availability sampling (DAS) from modular DA layers can reduce costs. The architecture must also plan for fault proofs (if optimistic) or proof aggregation (if ZK) to scale throughput. Testing should involve adversarial scenarios, such as a sequencer attempting to submit an incorrect inference result, to ensure the system's security guarantees hold.

An AI inference rollup is a specialized Layer 2 (L2) blockchain that executes AI model computations off-chain and posts the results to a Layer 1 (L1) like Ethereum for final settlement. The primary goal is to overcome the prohibitive cost and latency of running large models directly on an L1. The architecture must be designed around three core pillars: verifiable computation to ensure correctness, efficient data availability for model inputs/outputs, and a decentralized prover network to scale proof generation. Key protocols in this space include EigenLayer, which provides restaking for decentralized provers, and Celestia, which offers optimized data availability layers.

The execution environment is the heart of the system. Unlike a general-purpose EVM rollup, an AI rollup typically uses a zkVM (Zero-Knowledge Virtual Machine) or a dedicated zk-circuit tailored for neural network operations. Frameworks like RISC Zero or SP1 allow developers to write provable inference logic in Rust or C++. The process involves: 1) loading a pre-trained model (e.g., an ONNX file), 2) accepting user input data on-chain, 3) executing the model inference off-chain, and 4) generating a ZK-SNARK or ZK-STARK proof that attests to the correct execution. This proof is then verified by a smart contract on the L1.

Data availability and sequencing are critical for performance and cost. User transactions containing inference requests are batched by a sequencer. To minimize L1 gas fees, only essential data—the state root and the zk-proof—is posted on-chain. The full transaction data (the input prompts) can be posted to a modular data availability (DA) layer like Celestia or EigenDA. This separation ensures data is available for fraud proofs or re-execution while keeping Ethereum calldata costs low. The sequencer can be permissioned initially but should evolve towards decentralization using mechanisms like proof-of-stake sequencing.

A decentralized prover network is essential for scalability and censorship resistance. A single prover creates a bottleneck. The architecture should incentivize a network of nodes to participate in proof generation, possibly through a proof marketplace. Nodes can specialize in different model types (e.g., LLMs vs. image generators). Projects like RiscZero's Bonsai network demonstrate this approach. The economic security of the system can be enhanced by leveraging restaking via EigenLayer, where operators stake ETH or LSTs to act as provers or sequencers, with slashing conditions for malicious behavior.

The final architectural component is the settlement and verification layer on the L1. A smart contract, often called the verifier contract, holds the canonical state root and verifies the submitted zk-proofs. It also handles bridge contracts for asset transfers to and from the L2. The trust model shifts from trusting individual operators to trusting the cryptographic soundness of the zk-proof system and the economic security of the underlying data availability layer. This design allows the AI rollup to inherit the security of Ethereum while providing throughput that is orders of magnitude higher for AI-specific workloads.

A rollup sequencer for AI inference must be designed to handle a unique workload profile. Unlike typical DeFi transactions, AI inference requests are computationally intensive, variable in duration, and often stateless between requests. The primary architectural goal is to maximize throughput and minimize L1 settlement costs by batching multiple inference jobs into a single compressed proof. This requires a sequencer that can efficiently queue requests, manage a pool of GPU or TPU workers, and coordinate the generation of validity or zero-knowledge proofs for the aggregated results.

The sequencer's core logic involves several key functions. It must receive and validate incoming user transactions, which typically specify an AI model identifier (e.g., Llama-3-8B) and input data. These transactions are placed into a pending pool. The sequencer then employs a batching strategy—such as time-based (e.g., every 2 seconds) or size-based (e.g., up to 50 requests)—to group jobs. For cost efficiency, it's critical to batch jobs for the same model, as proving circuits are model-specific. The sequencer dispatches the batched input data to off-chain prover networks or dedicated hardware for execution and proof generation.

After the inference results and their corresponding validity proofs are generated off-chain, the sequencer prepares the batch for L1 settlement. This involves creating a batch transaction that contains the minimal required data: the new state root, a cryptographic commitment to the batched inputs and outputs, and the proof. Using compression techniques like data availability sampling or storing only state diffs on-chain can reduce gas costs by over 90%. The sequencer finally submits this batch to the rollup's smart contract on the base layer (Ethereum, Celestia, etc.), finalizing the transactions.

Implementing a basic sequencer involves smart contract and off-chain service components. The on-chain RollupContract might have a submitBatch function that only the sequencer can call. Off-chain, you can use a Node.js or Go service that listens for events, manages a queue with Bull or RabbitMQ, and interfaces with a proving service like Risc Zero or SP1. Here's a simplified pseudocode structure for the batch loop:

javascriptCopy
async function batchLoop() {
  const pendingTxs = await queue.getJobsForModel('stable-diffusion');
  if (pendingTxs.length >= BATCH_SIZE) {
    const {proof, newStateRoot} = await proverService.executeAndProve(pendingTxs);
    await rollupContract.submitBatch(newStateRoot, proof);
  }
}

Key design considerations include decentralization and liveness. A single sequencer creates a central point of failure. Architectures like shared sequencer networks (e.g., based on Espresso or Astria) or permissionless sequencing through proof-of-stake can mitigate this. Furthermore, the sequencer must be resilient to model execution failures and have a fallback mechanism for proof generation. The choice of proving system—ZK or optimistic—will also dictate sequencer design, as ZK rollups require immediate proof generation while optimistic rollups allow for a dispute window, shifting computational load.

Finally, the sequencer must expose APIs for users and indexers. A public JSON-RPC or REST API allows users to submit inference transactions and query status. An indexer or graph node should process batch submissions to make inference results queryable off-chain, as storing full outputs on L1 is prohibitively expensive. By carefully designing the sequencer around batch efficiency, proof integration, and robust APIs, you create the foundation for a scalable and economically viable AI inference rollup.

The AI execution runtime is the smart contract system deployed on your rollup's base layer (e.g., Arbitrum Nitro, OP Stack, or a custom settlement layer). Its primary function is to receive batched inference tasks, dispatch them to off-chain AI inference nodes, and verify the correctness of the returned results. Unlike a general-purpose EVM, this runtime is specialized; it doesn't execute the AI model itself but manages the lifecycle of an inference request and enforces cryptographic verification, typically using zero-knowledge proofs (ZKPs) or optimistic fraud proofs. This separation of execution (off-chain) and verification (on-chain) is the key to achieving scalable, low-cost AI inference.

Architecturally, the runtime consists of several core modules. A Dispatcher/Scheduler contract receives requests, batches them for efficiency, and assigns them to registered inference nodes. A Verification module is the heart of the system; for a ZK-rollup, this would verify a ZK-SNARK or ZK-STARK proof attesting to the correct execution of the model against specified inputs. For an optimistic rollup, it would manage the challenge period for fraud proofs. A State manager tracks the commitments (hashes) of model parameters and maintains a verifiable data availability layer, often using blobs or a dedicated data availability committee, to ensure input data and model weights are accessible for verification.

Implementing the verification logic requires integrating a proving system. For a zkML (zero-knowledge machine learning) approach, you would use frameworks like EZKL, RISC Zero, or zkML libraries to generate a circuit from your model (e.g., a TensorFlow or PyTorch graph). The runtime's verifier contract is then compiled from this circuit's verification key. A simplified interface in Solidity might look like this:

solidityCopy
contract AIVerifier {
    function verifyInference(
        bytes calldata _proof,
        uint256[] calldata _publicInputs // e.g., input hash, output hash
    ) public view returns (bool) {
        // Logic to verify the ZK proof
        return verify(_proof, _publicInputs);
    }
}

The _publicInputs are the elements both the prover and verifier agree on, such as a hash of the input data and the resulting output.

To ensure performance and reliability, the runtime must manage its off-chain oracle network of inference nodes. This involves a staking and slashing mechanism within the runtime's Node Registry to penalize malicious or offline nodes and reward honest ones. Nodes must stake collateral (e.g., in the rollup's native token) which can be slashed if they provide an incorrect proof or fail to respond. The runtime should also implement proof aggregation where possible, allowing a single proof to verify a batch of hundreds of inferences, dramatically reducing the on-chain verification cost per task.

Finally, the runtime exposes a clean Developer API for dApps. This typically includes a function to submit an inference request with encrypted input data, a function to query the status or result of a request, and event emissions for important state changes. By abstracting the complexity of proof generation and node coordination, this API allows developers to integrate scalable AI features—like an image-generation engine or a prediction market's oracle—into their applications with just a few lines of code, paying only for the verification gas cost on the rollup.

Data availability (DA) is the guarantee that transaction data is published and accessible to all network participants. For an AI inference rollup, this extends beyond simple transaction data to include the model weights and the input data for each inference request. The sequencer must post this data to a DA layer so that verifiers can independently reconstruct the rollup's state and validate the correctness of the AI's output. Without reliable DA, the system reverts to a trust-based model, negating the security benefits of a rollup.

You have several architectural choices for the DA layer, each with distinct trade-offs between cost, security, and speed. The most secure option is to post data directly to the parent chain (e.g., Ethereum), using calldata or blobs via EIP-4844. This inherits the full security of Ethereum but at a higher cost per byte. Alternatives include validium solutions like EigenDA or Celestia, which post data to a separate, dedicated network, significantly reducing costs while introducing a light trust assumption regarding data availability itself.

The data structure you publish is crucial. A typical commitment includes: the compressed model checkpoint identifier, the serialized input tensor data, and the computed output tensor. This bundle is often hashed into a single root (e.g., a Merkle root) that is posted on-chain. Off-chain, the full data is made available via a peer-to-peer network or a dedicated data availability committee. Verifiers download this data using the on-chain root as a commitment, ensuring they are verifying the correct computation.

For cost-efficient, high-throughput inference, a hybrid approach is often optimal. You might post small, frequent state updates (like inference results) via cheap blobs on Ethereum, while storing the large, static model weights on a decentralized storage network like Arweave or IPFS, referenced by a content identifier (CID) in the rollup's data commitment. This separates the dynamic inference traffic from the static model data, optimizing for both security and scalability.

Implementing this requires careful engineering. Your rollup's node software must have components for: data publishing (sequencer-side), data retrieval (verifier-side), and fraud proof construction that can leverage the retrieved data. A common pattern is to use a data availability challenge period during which verifiers can flag missing data, triggering a slashing condition for the sequencer if the data is not produced.

Settlement layer verification is the core security mechanism of any optimistic rollup. For an AI inference rollup, this means the base layer (e.g., Ethereum) must be able to verify that the state transitions published by the sequencer—representing batched AI model outputs—are correct. We implement this using a fraud proof system. After the sequencer posts a state root and associated data to an on-chain contract (the RollupContract), there is a challenge window (typically 7 days) during which any honest participant can dispute an invalid state transition by submitting a fraud proof.

The verification logic is encapsulated in a smart contract on the settlement layer. Its primary function is to adjudicate disputes. A fraud proof must provide the minimal data needed to reconstruct and re-execute a specific transaction within a batch. For an AI inference, this includes the input tensor, the model's parameters (or a commitment to them), and the claimed output. The contract then uses a verification function—often implementing a zk-SNARK verifier or a succinct fraud proof scheme like Cannon—to check the computation. If the proof is valid, the fraudulent state root is rejected, and the challenger is rewarded.

Designing the data availability layer is critical for this process. Verifiers must be able to reconstruct the state of the rollup to check claims. We recommend using EigenDA or Celestia as dedicated data availability layers, or Ethereum blobs via EIP-4844. The sequencer posts transaction data here, and its availability is verified with Data Availability Sampling (DAS). The on-chain contract stores only a pointer (like a KZG commitment) to this data. This separation keeps L1 costs low while ensuring verifiers can access the data needed to construct fraud proofs.

To implement this, you'll write the core RollupContract. Key functions include submitBatch(bytes32 stateRoot, bytes calldata dataCommitment) for the sequencer and challengeStateTransition(uint256 batchIndex, FraudProof calldata proof) for verifiers. The fraud proof struct must contain the pre-state, post-state, and the specific transaction data for the disputed inference. The contract's verification function will call a precompiled verifier contract for your chosen proof system (e.g., a Plonk or Groth16 verifier).

Finally, you must run a verifier node alongside your sequencer. This node continuously monitors the rollup and settlement layer contracts, downloading batch data and independently verifying state transitions. If it detects an error, it constructs and submits a fraud proof. Using a framework like OP Stack or Arbitrum Nitro can abstract much of this complexity, providing battle-tested fraud proof systems and contract templates that you can customize for your AI-specific state transition function.

Architecting a rollup for AI inference requires a specialized stack. The foundation is a data availability layer like Celestia or EigenDA to post transaction data cheaply. The execution layer must run a zkVM (e.g., RISC Zero, SP1) or a custom zk-circuit for model inference, generating validity proofs. A high-throughput sequencer orders transactions, while a decentralized prover network, potentially using proof aggregation, handles the computational load. Finally, smart contracts on a settlement layer (like Ethereum) verify proofs and manage state updates.

For implementation, start with a framework. Rollkit provides a modular framework for building rollups with custom execution environments. The Cartesi Rollups framework allows you to write application logic in Rust or Python and run it inside a RISC-V machine, which is well-suited for AI workloads. Alternatively, you can use ZK Stack from zkSync to create a hyperchain, though you'll need to integrate custom proving for AI ops. Your first milestone should be a testnet that can execute a simple model, like a small neural network for MNIST classification, and produce a validity proof.

Key performance optimizations to research next include proof recursion to aggregate multiple inference proofs into one, significantly reducing on-chain verification costs. Explore model quantization and pruning within the zk-circuit to minimize the number of constraints. For specific use cases like LLM inference, investigate stateful rollups where the model's parameters are stored on-chain and only the input prompts and output logits are proven, reducing per-transaction overhead. The Espresso Systems sequencer offers shared sequencing that can improve cross-rollup interoperability for AI agents.

The future of AI rollups lies in specialized application-specific rollups (AppRollups). Instead of a general-purpose chain, build a rollup tailored for a single AI service—like a decentralized image generator or an on-chain verifiable chatbot. This allows for maximal optimization of the proof system and sequencer logic for that specific workload. Monitor projects like Modulus Labs, which focuses on ZKML, and Giza, which is building tooling for on-chain machine learning, to stay current with best practices and new primitives.

Your development roadmap should prioritize: 1) Selecting and integrating a proving system, 2) Building a sequencer that batches inference requests, 3) Implementing a data availability bridge, and 4) Creating developer tooling (SDKs, indexers). The end goal is a scalable, trust-minimized platform where AI inference is as verifiable and composable as any other on-chain transaction, unlocking new paradigms for decentralized AI applications.