> open weights, open code and open data
Even if you have all these things you still can't replicate a model because of randomness.
You can backdoor a model with less than 1000 examples and it is impossible to detect.
You can take the code for Kimi K3 now, take the training framework from Prime and the data from Olmo, spend some money on RL environments and some more money (!) on GPU training and end up with a system of similar capabilities.
But that's completely different to being able to audit Kimi K3. Even if you had the exact code, data and training environments it is impossible to verify that the model you have came from that.
They just don't work at all on a many month long, 100K+ GPU cluster training run.
Even then you'd still need to account for order of events when an entire cluster of GPUs is involved. Also don't forget to account for any synthetic data sources. Or even non-synthetic for that matter - does your pipeline do any image resizing on the fly? Better make sure that's fully deterministic between machines (it almost certainly won't be).
It's theoretically possible but I don't expect it to materialize any time soon.
I mean I guess, but not in a performant way if there are ever any hardware failures. And with 100K GPUs there are multiple hardware failures per day.
Where is this meme coming from? IEEE floating point is deterministic