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I am happy to share FlashProve: our performant open-source prototype for provable inference. We believe that this has significant implications for security, pacing AI development, integrity guarantees, and more. FlashProve allows a provider to commit to model weights, and then prove that replies originate from the committed model. The... show more

Giacomo Fenzi

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12,157 views • 3 days ago •via X (Twitter)

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Giacomo Fenzi3 days ago

FlashProve is a commit-and-prove succinct zero-knowledge argument. The provider commits to the model weights once, and sends the commitment to clients. When a client submits a prompt, the prover attaches a short proof, and the client (on consumer-hardware) can check the proof against the commitment. The proof cryptographically guarantees that the provider used the model claimed to generate the response, and the provider is assured that it does not leak information about the weights. Further, the proof is succinct: it is short and the verifier can efficiently check it on consumer hardware.

Giacomo Fenzi3 days ago

To build FlashProve, we rely on battle-tested cryptography: interactive oracle proofs and cryptographic hash functions. An interactive oracle proof is an interactive proof system, in which a prover sends long oracle messages which the verifier can query. Using Merkle commitments, we replace the oracles with cryptography, and we use Fiat-Shamir to remove interactivity. These succinct arguments have many desirable properties: minimal assumptions, post-quantum security, and fast provers. We tailor our interactive oracle proof to machine learning computation, and achieve succinctness via fold and hopping, an adaptation of the techniques in Zook.

Giacomo Fenzi3 days ago

To achieve great performance, we had to complement this with a large amount of engineering. Safe to say, Claude was very happy to help! We prove the entire model streaming on a single GPU (which cannot even fit the weights!). For a 400B mixture-of-experts model, we can prove at more than 100tok/s, just two orders of magnitudes slower than running the model natively! We are aware of a number of further optimizations, so expect the number to improve!

Giacomo Fenzi3 days ago

We have (many) more details in the blog posts and the repositories. We also expect to post a (human generated) paper explaining the protocol in more details soon, so stay tuned for that one as well. FlashProve wouldn’t have been possible without the excellent work of everyone involved: @RoyRinberg , Alex, Ale, @miranda__christ, Rahul, @transmissions11 and @gwberg95 (and Claude). It was a lot of fun!

Jens Groth2 days ago

Very cool, congrats! @GiacomoFenzi how do you handle noise in its various forms? Is my understanding correct that you make the model deterministic, i.e., no race conditions and no floating point issues? How about other input that may sneak in: time, tool calls, prompt grinding?

Giacomo Fenzi2 days ago

Yes! Currently for the prototype we quantize the model to make it deterministic. We have a few other tricks in store for the general case, that we hope to share soon.

Georgios Konstantopoulos3 days ago

congrats man!! Awesome work.

Giacomo Fenzi2 days ago

Thank you!

drouyang3 days ago

Congrats! Do you expects further performance improvements from the protocol or the GPU prover implementation side?

Giacomo Fenzi2 days ago

Thank you! Both! I think there is juice to squeeze in many places

drouyang2 days ago

Great to know. Cann’t wait to build on top of it!

Princess Vegeta3 days ago

oooo

pietro3 days ago

@GiacomoFenzi goated

Giacomo Fenzi2 days ago

gotta cook some time 🧑‍🍳

Soubhik Deb2 days ago

wanted to confirm if i am understanding this part correctly: the models are being quantized to integer only to get determinism in matrix multiplication (due to arithmetization being over only integer fields?) and that there is another check to ensure that error introduced due to integer quantization is not being used as cover for breaking soundness?

Robert Corbin | Shadowfetch Studio | AIpeoplewiki3 days ago

Congrats on shipping FlashProve, Giacomo! Really cool to see this open-sourced. #AI #OpenSource

osdnk2 days ago

@claucece Wow, that's so good!

Giacomo Fenzi2 days ago

@claucece Thank you! Very very happy ;)

Loris3 days ago

Awesome stuff! Truly incredible to see zk proofs to secure llm!

Giacomo Fenzi2 days ago

Thank you!

Francesco Intoci2 days ago

Lo chef

Giacomo Fenzi2 days ago

🧑‍🍳🧑‍🍳🧑‍🍳

AndrewJoes2 days ago

Your report says FlashProve generates proofs after replies are served, by recomputing transcripts on separate hardware. A valid proof shows what the committed model would output. It doesn’t establish which model actually served those tokens. How can computational consistency prove the response’s origin?

Shez Malik2 days ago

didn't think my laptop could do this

Zeko Labs2 days ago

nice work. we just open sourced our private, sovereign ZK AI protocol for ethereum

JB2 days ago

One inference proved is a big step. Agent runs are dozens of calls with tool results feeding back in between. Curious how you see per-call proofs composing into evidence of the whole run, including the non-model steps a verifier still has to take on trust.

0xJoshAlpha2 days ago

provable inference only matters if the prover cannot swap the model mid-run

Shavaiz Tariq3 days ago

the sampling part is where i'd poke. with temperature above zero, a provider could swap in a cheaper model and claim a lucky sample unless the seed is committed too. is the randomness bound into the proof, or is this greedy decoding only?

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