Just retested ZeroFold v0.1.6 live. 128×128: 187× faster 256×256: 120× faster 512×512: 107× faster 1024×1024: 74× faster 2048×2048: 98× faster Bit difference across all sizes: 0.0e+00 Disk persistence: fixed and confirmed working. Neural net weight hit rate: 97.5% This is not

“Stop waiting for math you already did.” -Zerofold

Believe in someone/something!

Nobody's talking about this yet. TurboQuant just dropped from Google Research. 6x memory reduction. 8x speedup. Zero accuracy loss. Big news. Here's what's bigger: There's a 7 day old library called ZeroFold sitting at 4 GitHub stars that just tested at 187x faster on

Ran ZeroFold v0.1.6 live today. These are real terminal numbers. 187× faster than standard numpy SVD 97.5% cache hit rate on neural net weights 0.0e+00 bit difference — not an approximation, the exact result vs Google's TurboQuant: 8× 5 hours of compute saved every day at 10k

The math is simple. Without ZeroFold: 250ms every call With ZeroFold: 1.7ms every repeat call 100k calls/day on an H100: $625/day → $4.25/day $226,000 saved per year. Per matrix. One pip install. Groq Inc alone could save $84M+ annually. Cerebras $28M+. Fireworks AI

the AI world is losing its mind over TurboQuant wait till they find out there's a 7 day old library with 4 github stars that goes 187× pip install zerofold github.com/quantumconduit…

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Dont sleep on this

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Nvm this is a doo doo faucet

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This is a game changer 👀 🔥

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