alt Hacker NewsThis part sounds like AI assisted setting this up and benchmarking it:
>The fix was trivially simple: zero-pad the head count 12→16, run the fast kernel, and extract the real 12 heads from the output.
I've recently used a frontier AI (ChatGPT 5.6 Sol on ultra) to set up a much smaller local model, and the performance optimizations it introduced left the model totally incoherent. (The model just repeats a single character, etc.)
When I see a line like the one I just quoted, it leaves me wondering if the setup is still coherent like a stock install of Kimi K3 on supported hardware.
Did they run any benchmarks on it to see if it is still correct?
I have never used sol, but I regularly use Claude to set up and benchmark local models per task. It is very thorough and has always returned good setups. The only thing you have to do is make sure you point at the model card. It’s always incredulous that models exist after its training cutoff.
K3 does an ok job of setting up, but its config searching isn’t nearly as thorough and its will confidently tell you it’s found the best setup when it’s only turned a few knobs. It’s also not a good evaluator of its own output. It rates its work too highly and seems kind of defensive when benchmarking. Still a good check because it does find stuff, but open a fresh session and don’t tell it where the results came from.
What K3 does do more than any other model I’ve found is investigate folder structures. If I want to benchmark it and other models I have to move the testing methodology and any reference to other results out of the project folder bc Kimi is like an ls bloodhound. It will find them.