TurboQuant
Indie Hackers8.5x LLM KV-cache compression with zero quality loss
Compress LLM memory 8.5x without losing quality. Problem: LLM inference costs $10k+/month because KV cache uses all GPU memory. Solution: I built TurboQuant using Google DeepMind's algorithm. Result: - 256MB → 30MB me
Share cardActual performance
Did not reach leaderboard
Launch Intel predictions
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Product HuntOn track for Day 1 leaderboard · Strong signals: google, using · Missing: mac, agents, macos64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released60%60% predicted probability of success on Hacker News, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: month, google · Missing: mobile apps, ios, personal58%58% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive55%55% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting54%54% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto8%8% predicted probability of success on BetaList, based on ML models trained on real launch data.
Incorrect prediction on native model
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