alt Hacker NewsThe main benefits are:
1. it proves your models are running correctly. FP ops tend to deviate in small amounts between hardware providers and tensor ops even more so due to the relatively new and unstable standardisation environment atm. So it proves your HW configuration is delivering outputs correctly. i.e. it's a good test for what is currently a very non-trivial validation environment.
2. it proves your external provider is running the inference they said they are. i.e. it eliminates the concerns of model providers running quantized versions silently behind the scenes since quantization breaks the proof and you need to use a new, updated proof signature for the quantized version.
3. likewise for fine tuned or tampered models. Or outright substitution for another model (ex the accusation by OAI and Anthropic that Kimi and DS and co are periodically forwarding requests to their platforms to harvest responses for knowledge distillation).
All in all it's a nice chain of trust utility if it can evolve out of the toy stage and reach production viability (since rn it's limited to int-only weights and 1k context which is practically useless atm).
Replies
Also useful to audit in case we pause the development of AI and we need to check inference is being done with known models.
1. That sounds like an Anthropic concern, not a concern for most customers who don't run their own model. 2. This still requires trust in the provider, I imagine these proofs would change weekly as they tweak their models, and you have zero knowledge of what the changes are, you just know you're getting version 11.12.1999 - does that really help you if the provider is maliciously downgrading after release? All you know is you got the same version as everyone else, not what that version is. 3. Again, if you don't trust the provider, it's game over - you're sending them all of your data. Knowing a version number is not going to help you much.
I'm not convinced this is useful for problems most people face using models, it seems a very niche academic concern frankly and an incredibly complex system to prove not very much.