The Cost of Inefficient Proof Generation in zkML's Adoption

Proof generation times for complex models can span minutes to hours, making applications like autonomous agents or on-chain gaming non-viable. This latency stems from the sequential nature of proof systems like Groth16 and the massive computational graphs of neural networks.

• Real-time inference requires sub-second proofs.
• Current state lags by orders of magnitude, creating a fundamental adoption barrier.

High computational overhead translates directly to high user cost. Proving a single inference from a model like ResNet-50 can cost $1-$10+ in equivalent compute, dwarfing the cost of the ML operation itself.

• Makes micro-transactions and high-frequency use economically impossible.
• Creates a negative feedback loop where high cost suppresses demand, preventing scale economies.

zkSNARK circuits struggle with non-linear operations (e.g., ReLU) and large parameter sets. This forces developers to use severely truncated models or novel proof systems like zkSNARKs with GPU acceleration (e.g., zkCUDA) which are still nascent.

• Sacrificing model accuracy for provability defeats the purpose.
• The ecosystem lacks standardized tooling (a la PyTorch) for easy zkML compilation.

New proving systems like Plonky2 and Nova enable recursive proof composition and parallelization. Specialized hardware (ASICs, FPGAs) and GPU-accelerated provers from firms like Ingonyama and Cysic aim for 100-1000x speedups.

• Recursive proofs allow splitting large models into manageable, parallelizable chunks.
• Hardware acceleration is the only path to reaching cost parity with traditional cloud inference.

Frameworks like EZKL, zkml, and Giza are creating higher-level abstractions. The key is quantization (reducing numerical precision) and pruning (removing unnecessary model weights) to shrink circuit size without catastrophic accuracy loss.

• Model-to-circuit compilers automate optimization.
• Approximate computing trades marginal precision for massive efficiency gains.

Adoption requires aligning costs with value. Shared prover networks (similar to EigenLayer for AVS) can amortize fixed hardware costs across many users. Intent-based architectures (like UniswapX) could batch user inferences for a single, cheaper proof.

• Proof marketplace dynamics drive cost down via competition.
• Batching turns variable costs into fixed, scalable overhead.

Proving an AI inference can cost 100-1000x the compute cost of just running it. Modulus builds specialized provers (like Remainder) that optimize for ML workloads, not generic circuits.

• Key Benefit: ~10x cost reduction for on-chain AI by optimizing for tensor operations.
• Key Benefit: Enables verifiable inference for models up to ~1B parameters, moving beyond toy examples.

Using a general-purpose zkVM like RISC Zero's zkVM for ML is like using a Swiss Army knife for surgery—possible, but inefficient. It provides flexibility but pays a heavy performance tax.

• Key Benefit: Developer accessibility—any Rust code can be proven, lowering the zkML entry barrier.
• Key Benefit: Creates a universal proof layer but at the cost of ~1000x slower proof times versus native execution for complex ML.

High-level frameworks like EZKL and Giza abstract circuit writing, but they generate sub-optimal, bloated circuits. This abstraction layer introduces massive overhead versus hand-optimized, domain-specific circuits.

• Key Benefit: Rapid prototyping—turn a PyTorch model into a circuit in minutes.
• Key Benefit: Democratizes zkML creation but currently results in proving costs 50-200x higher than the theoretical optimum.

The ultimate bottleneck is silicon. Ingonyama and others are designing zk-ASICs (like the ICICLE GPU library) to accelerate MSMs and NTTs—the core cryptographic operations in proving.

• Key Benefit: 100-1000x acceleration of prover performance at the hardware level, the only path to consumer-scale zkML.
• Key Benefit: Shifts the competitive moat from algorithms to physical hardware and proprietary silicon architectures.

zkML only makes economic sense when the cost of verification + proving is less than the value of the fraud it prevents. Current proving costs of $1-$10+ per inference kill most use cases.

• Key Benefit: Defines the clear performance benchmark prover stacks must hit: sub-cent proof costs.
• Key Benefit: Forces a focus on selective disclosure—proving only the necessary computation to minimize circuit size.

Platforms like Succinct and SP1 are not building end-user provers but the zkVM infrastructure (like SP1) and proving marketplace (Succinct's Prover Network) to aggregate demand and optimize hardware utilization.

• Key Benefit: Economies of scale—a shared network reduces idle time for expensive GPUs/ASICs.
• Key Benefit: Abstraction layer that lets application developers (e.g., Axiom, Brevis) outsource the proving complexity.