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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

1,812 subscribers

12,157 Aufrufe • vor 3 Tagen •via X (Twitter)

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28 Kommentare

Profilbild von Giacomo Fenzi

Giacomo Fenzivor 3 Tagen

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.

Profilbild von Giacomo Fenzi

Giacomo Fenzivor 3 Tagen

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.

Profilbild von Giacomo Fenzi

Giacomo Fenzivor 3 Tagen

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!

Profilbild von Giacomo Fenzi

Giacomo Fenzivor 3 Tagen

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!

Profilbild von Jens Groth

Jens Grothvor 2 Tagen

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?

Profilbild von Giacomo Fenzi

Giacomo Fenzivor 2 Tagen

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.

Profilbild von Georgios Konstantopoulos

Georgios Konstantopoulosvor 3 Tagen

congrats man!! Awesome work.

Profilbild von Giacomo Fenzi

Giacomo Fenzivor 2 Tagen

Thank you!

Profilbild von drouyang

drouyangvor 3 Tagen

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

Profilbild von Giacomo Fenzi

Giacomo Fenzivor 2 Tagen

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

Profilbild von drouyang

drouyangvor 2 Tagen

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

Profilbild von Princess Vegeta

Princess Vegetavor 3 Tagen

oooo

Profilbild von pietro

pietrovor 3 Tagen

@GiacomoFenzi goated

Profilbild von Giacomo Fenzi

Giacomo Fenzivor 2 Tagen

gotta cook some time 🧑‍🍳

Profilbild von Soubhik Deb

Soubhik Debvor 2 Tagen

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?

Profilbild von Robert Corbin | Shadowfetch Studio | AIpeoplewiki

Robert Corbin | Shadowfetch Studio | AIpeoplewikivor 3 Tagen

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

Profilbild von osdnk

osdnkvor 2 Tagen

@claucece Wow, that's so good!

Profilbild von Giacomo Fenzi

Giacomo Fenzivor 2 Tagen

@claucece Thank you! Very very happy ;)

Profilbild von Loris

Lorisvor 3 Tagen

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

Profilbild von Giacomo Fenzi

Giacomo Fenzivor 2 Tagen

Thank you!

Profilbild von Francesco Intoci

Francesco Intocivor 2 Tagen

Lo chef

Profilbild von Giacomo Fenzi

Giacomo Fenzivor 2 Tagen

🧑‍🍳🧑‍🍳🧑‍🍳

Profilbild von AndrewJoes

AndrewJoesvor 2 Tagen

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?

Profilbild von Shez Malik

Shez Malikvor 2 Tagen

didn't think my laptop could do this

Profilbild von Zeko Labs

Zeko Labsvor 2 Tagen

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

Profilbild von JB

JBvor 2 Tagen

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.

Profilbild von 0xJoshAlpha

0xJoshAlphavor 3 Tagen

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

Profilbild von Shavaiz Tariq

Shavaiz Tariqvor 3 Tagen

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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