Show HN: TurboQuant-WASM – Google's vector quantization in the browser

teamchong 148 points 6 comments April 04, 2026
github.com · View on Hacker News

Discussion Highlights (4 comments)

hhthrowaway1230

Awesome! Also love the gaussian splat demo, cool use case!

glohbalrob

Very cool. I added the new multi embedding 2 model to my site the other week from google I guess need to dig into this and see if it’s faster and has more use cases! Thanks for publishing your work

refulgentis

Sloppiest slop I've seen in a couple weeks: - fork of a fork of a quantization technique - Only contribution is...compiling JS to WASM by default? - suspicious burst of ~nothing comments from new accounts - 6 comments 7 hours in, 4 flagged/dead, other 2 also spammy, confused and making category errors at best, at worst, more spam. - Demo shows it's worse : 800 ms instead of 2.6 ms for text embedding search - "but it saves space" - yes! 1.2 MB in RAM instead of 7.2 MB to turn search into 1s on a MacBook Pro M4 Max, instead of sub-frame duration. - It's not even wrong to do this with the output embeddings, there's way more obvious ways to save space that don’t affect retrieval time this much

netdur

I tried TQ for vector search and my findings is not good, it is not worth it if you cannot use GPU, however I got same quality of search as 32f using 8bit quant I wrote ann ext for sqlite, using tq, I do save a lot on space but 32f is still faster despite everything I have tried code here https://github.com/netdur/munind/tree/main/src/tq

Semantic search powered by Rivestack pgvector
3,558 stories · 33,161 chunks indexed