Checkpoint-replay verification of a training run is not merely buggy: making it provably robust reduces to open problems in learning theory, and spoofing already forges valid logs cheaply. The only floor that does not rest on unsolved theory is a zero-knowledge proof of training.
A proof of inference certifies an output came from a committed model. It says nothing about how the weights came to be. Proving the training run is a separate, far heavier object — and it is the one compliance actually asks for.
Intuition says the FLOP-heavy matmuls dominate a zkML proof. Multiple 2024-2026 systems show the opposite: linear layers are cheaper to prove than to compute, and the bill is set by activations multiplied by their bit-width.
On real networks the dominant zkML prover cost is not the matmuls or the activations. It is the consistency check binding the proof to the committed weights — an overhead that grows past 90% of prover time and that the single benchmark number hides.
Pricing a zero-knowledge proof of an LLM gives an absurd number, and teams conclude zkML is not ready. They are pricing the wrong model. For small fixed models on high-stakes decisions, zkML already pays for itself today.
DeepProve, from Lagrange, produced the first zero-knowledge proof of a full LLM inference — GPT-2. It moves "prove a transformer" from impossible to merely expensive. What that unlocks, and what is still years away.
Two ways to make an off-chain model output trustworthy on-chain. zkML is cryptographic, expensive, and small-model-only. opML is optimistic, cheap, and runs Llama-2-scale models today. Choosing by stakes, model size, and latency.
EZKL, Modulus, Giza, Ora, RISC Zero. Same model, same input, same target chain. Proof times, gas costs, gotchas — and the one we'd put in front of a customer.
zkML cannot scale to large models because proving a whole computation in one shot is ruinously expensive. Folding schemes — Nova and its lineage — prove a long, repetitive computation step by step instead. Explained without the cryptography.
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