HOhochmartinez·Apr 3, 2026Submission
10-19x faster alternative to TurboQuant via Clifford rotors (44x fewer params)
2 points · Open discussiontonbistudio
turboquant-pytorch
Python2026–2026
tonbistudio / turboquant-pytorch
turboquant-pytorch
From-scratch PyTorch implementation of Google's TurboQuant (ICLR 2026) for LLM KV cache compression. 5x compression at 3-bit with 99.5% attention fidelity.
3Distinct people
3Mentions
0Explicit recommenders
1KGitHub stars
What to know before exploring
What it is
From-scratch PyTorch implementation of Google's TurboQuant (ICLR 2026) for LLM KV cache compression. 5x compression at 3-bit with 99.5% attention fidelity.
GitHub
Where it is used
No documented product use has been verified yet.
What people are saying
No recommendation phrase was found in the indexed comments.
No classified warning is attached to this page. Read the discussions before treating silence as approval.
Also in these discussions
No other repository is repeatedly linked in the same discussions.
This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.
No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.
Mentions over time
HN mentions by day3 dated mentions
Observed mentions: 2026-03-25 11:48:50 UTC to 2026-04-03 21:59:20 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| Mar 25 | 2026-03-25 00:00:00 UTC | 2026-03-26 00:00:00 UTC | 1 | 0 | 1 |
| Mar 26 | 2026-03-26 00:00:00 UTC | 2026-03-27 00:00:00 UTC | 0 | 0 | 0 |
| Mar 27 | 2026-03-27 00:00:00 UTC | 2026-03-28 00:00:00 UTC | 1 | 0 | 1 |
| Mar 28 | 2026-03-28 00:00:00 UTC | 2026-03-29 00:00:00 UTC | 0 | 0 | 0 |
| Mar 29 | 2026-03-29 00:00:00 UTC | 2026-03-30 00:00:00 UTC | 0 | 0 | 0 |
| Mar 30 | 2026-03-30 00:00:00 UTC | 2026-03-31 00:00:00 UTC | 0 | 0 | 0 |
| Mar 31 | 2026-03-31 00:00:00 UTC | 2026-04-01 00:00:00 UTC | 0 | 0 | 0 |
| Apr 1 | 2026-04-01 00:00:00 UTC | 2026-04-02 00:00:00 UTC | 0 | 0 | 0 |
| Apr 2 | 2026-04-02 00:00:00 UTC | 2026-04-03 00:00:00 UTC | 0 | 0 | 0 |
| Apr 3 | 2026-04-03 00:00:00 UTC | 2026-04-04 00:00:00 UTC | 1 | 1 | 0 |
What Hacker News says
3 sourcesThese "truths" are more like concerns.
Skills do atrophy if you don't practice them, but also, refreshing your memory about some technology you haven't used in a while, or even learning something new, is easier than ever. You can ask the AI questions and try things out yourself very easily. Maybe "just in time" learning isn't good enough, but that's more of a concern based on speculation than a truth.
AI is being subsidized, but also, inference costs are dropping due to algorithmic improvements. For example, TurboQuant [1] looks pretty promising and even if it doesn't pan out, there are plenty of other potential advances like that. Competition might result in AI inference being available at good prices even without subsidies. So, again, more of a concern than a "truth."
Prompt injection: an unsolved mess, but perhaps curated, trusted data
Read the rest
sets will be good enough for many projects, so you don't have to expose your agent to the open Internet? It's a similar problem to the supply-chain vulnerabilities that downloadable open source libraries have. A valid concern, but it seems like we'll improve security and muddle through?
Copyright: also an unsolved mess, but kind of similar. Search engines copy the web as part of how they work, but that didn't stop Google from becoming big tech. And sure, Napster was built on copying music and was shut down, but YouTube was also built on widespread copyright violation and it muddled through. It's unclear whether copyright law is load-bearing infrastructure for the software industry or for open source software.
[1] https://github.com/tonbistudio/turboquant-pytorch
And a group has published an independent working implementation today, nice to see:
https://github.com/tonbistudio/turboquant-pytorch
In TurboQuant: Redefining AI efficiency with extreme compression