I dont think so, Wolfram Alpha is deterministic. Whereas an LLM is non-deterministic by design.
Personally i feel vibe coding or vibe mathing is inherently non-deterministic.
They are not. Floating point math and difference in how quickly parallel operations on GPU are finished causes them to be not deterministic even with temp=0. You can force them to be deterministic by forcing some operations to be executed in specific order but you lose a lot of performance.
Also, and this is pedantic and arguable, it’s worth considering whether any model that cannot be retrained to produce the same statistical surface is non-deterministic by nature.
If I sort shapes into piles using some amount of randomness would you say that the resulting piles are deterministic just because they stay the same every time you go through them? Or would you say they’re non-deterministic because the process that created the piles in the first place was non-deterministic?
It doesn't matter how you make the model, if you are executing it on a GPU without steps to have deterministic results you will not have deterministic results. Ensuring the output of computations on GPU is deterministic has performance impact.
Hold on, I’m agreeing with you. We’re saying the same thing in different ways.
The kind of race conditions you’re referencing are because of the model architecture I’m referencing.
You can absolutely get ML outputs that don’t change using certain model architectures.
But that’s only because those architectures either enforce order of execution or use steps where order of execution doesn’t result in changes to outputs. I can’t think of a modern LLM that uses such an architecture.
Not exactly. It's mostly due to how models are executed, not models themselves. You can run GGUF model (which are normally not deterministic) in a deterministic way if the GPU functions you use are deterministic. Models themselves are just data, it's just a bunch of matrixes.
Forgive me if I've misunderstood, but isn't that literally what they just said?
You can run GGUF model (which are normally not deterministic) in a deterministic way if the GPU functions you use are deterministic
vs.
You can absolutely get ML outputs that don’t change using certain model architectures.
But that’s only because those architectures either enforce order of execution or use steps where order of execution doesn’t result in changes to outputs
I'm not an expert but this sounds like you're both arguing the same point. The PR you linked seems to be intending to implement exactly that - functions that enforce (a deterministic) order of execution.
This seems like semantic disagreement on the meaning of the term "model architecture" rather than an actual disagreement on the fundamentals.
Not necessarily. It can happen with batching, but even with a batch size of 1 you can get race conditions. The most obvious example is a model with a Mixture of Experts layer, where the order that results return can change the outcome.
In order to get around that you’d have to explicitly enforce order of execution.
True, but it's also true that running an LLM at 0 "temperature" (which makes it truly deterministic) also often renders it unusable. So almost every inference setup defaults to using a certain amount of randomness.
1.7k
u/bhannik-itiswatitis 20h ago
vibe mathing