Your local AI just got up to 5x more memory. Same model. Same device. Nearly zero accuracy loss. QVAC SDK 0.12.0 integrates TurboQuant - Google Research's latest memory optimisation algorithm. What is TurboQuant? The KV cache is the memory your model uses to track a conversation. As context grows,... it fills up fast. 32K tokens. 64K. Game over. TurboQuant compresses it up to 5x with no accuracy loss. What does it unlock for you? Your app had a 16K token ceiling? It's now 96K. On the same device. Just update the QVAC SDK to get up to 5x more efficiency. No code changes. All from one SDK. The TurboQuant integration unlocks sovereign intelligence for more people, on more devices. Learn more →show more

If shipping things like TurboQuant sounds exciting to you & you want to push the frontier of local AI, QVAC is hiring an inference engineer. Apply here if interested:

Want to build local AI application ? Try out the QVAC SDK now →

TurboQuant shows small accuracy tradeoffs in longer contexts. The zero-loss claim does not match those results.

This is exactly where one of the most interesting directions for future AI begins to appear: not just bigger models, but: informational efficiency; coherent compression; distributed memory; and decentralized intelligence. TurboQuant is interesting because it attacks the real bottleneck directly: the KV cache quickly becomes the limiting factor for local context scaling. In NMSI/TQC logic, the idea could go even further: coherent oscillatory memory; phase/synchronization-based compression; geometric context addressing; and distributed processing across local photonic nodes. In practice: instead of brute-force storing every token, the system could preserve: coherent relations; semantic geometry; and resonant modes of the context. That would drastically reduce: memory requirements; energy consumption; and dependence on gigantic centralized data centers. And perhaps most importantly: AI would become far more: decentralized; sovereign; resilient; and locally accessible.

5x KV compression is not just “more memory” - it’s fewer compromises. Longer context → better reasoning → fewer round trips → lower cost. For apps like ours building on QVAC, this directly translates to smarter agents on-device, not in the cloud. This is a big shift.

KV cache wins are underrated; 5x on the same device

I'd rather use MemPalace V3.3.5 than a dehydrated, soul less TurboQuant.

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Nice optimisation! 🤔 Does this mean our models can handle way more data now without a major slowdown?

Cala a boca primo

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okayy

Google's been shopping TurboQuant to mobile chip makers for months. The real play isn't desktop - it's getting frontier models running locally on phones before Apple's Neural Engine v4 ships. That's why the sudden SDK release.

5x without accuracy loss? show the benchmarks

Really impressive work, let’s collaborate 🚀

Strong project, open for collaboration 🤝

Wow, that's actually pretty wild. More memory without killing accuracy sounds like a huge win for running stuff locally. Nice work.

5x more memory without changing your device is huge

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Gila, 5x lipat memory tanpa accuracy loss. Langkah gede buat AI di device lokal.

@grok 이들이 말하는게 정확히 무엇이죠? 쉽게 설명해주세요.