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Re:TurboQuant: Redefining AI efficiency with extreme compres... by akhenakh on Wednesday March 25, 2026 @08:46AM (#47516613)
Someone implementing it on llamacpp already https://github.com/mudler/llama.cpp/commit/dee102db1bfd723c9...
Re:TurboQuant: Redefining AI efficiency with extreme compres... by GistNoesis on Wednesday March 25, 2026 @10:27AM (#47517801)
He even attempts to improve on the paper by replacing the random rotation operation which is O(d^2), by a Subsampled Randomized Hadamard Transform which can be computed in O(d*log d).

Hopefully Johnson–Lindenstrauss lemma applies in the same way for SRHTransformed vectors as they do for randomly rotated vectors and the independence of the distribution laws of the coordinates remains and therefore the quantization of each coordinates independently is still theoretically sound.

Re:TurboQuant: Redefining AI efficiency with extreme compres... by cpburns2009 on Wednesday March 25, 2026 @09:00AM (#47516780)
For some reason I thought the implementation would be way more complicated than that. I obviously lack the domain knowledge to tackle something like this, but it looks straight forward.
Re:TurboQuant: Redefining AI efficiency with extreme compres... by qingcharles on Wednesday March 25, 2026 @12:37PM (#47519715)
Agreed. Actual LOC is tiny. Very impressive PR.
Re:TurboQuant: Redefining AI efficiency with extreme compres... by vibe42 on Wednesday March 25, 2026 @12:25PM (#47519537)
The pace of development in llama.cpp is really high, could see an implementation being merged in 4-6 weeks.