Tether AI announces integration of the open-source TurboQuant implementation in QVAC SDK 0.12.0. TurboQuant, originally proposed by Google Research, can compress the KV Cache memory required during large model operation by up to 5 times, enabling longer context, larger documents, and longer conversations to run on local devices. Tether states that this technology will be applicable to laptops, smartphones, edge devices, and decentralized AI networks, and is part of its strategy to promote localized and decentralized AI.

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ReflectionsOnTheStreetCorner
· 9h ago
If this technology route can compress it by 5 times while still maintaining accuracy, it will likely be quickly followed by mainstream frameworks.
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LeverageLatte
· 9h ago
Mobile device long document conversations finally no longer need to upload sensitive data to the cloud, privacy advocates are ecstatic
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MirrorBallReflection
· 9h ago
Does 5x compression mean my old laptop can also run the 7B model locally? Looking forward to QVAC 0.12.0
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GateUser-a9315d81
· 9h ago
KV Cache compressed by 5 times, how much will the inference latency increase? Is there a benchmark?
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GateUser-6857a9c9
· 9h ago
Decentralized AI networks require this kind of edge optimization, reducing both bandwidth and storage burdens.
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GateUser-665eb149
· 9h ago
Google Research's foundation + the implementation of Tether, this combo is quite interesting
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ContrarianIndicatorBonsai
· 9h ago
Finally able to run long context on mobile phones, TurboQuant's compression ratio is indeed impressive.
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PerpetualKing
· 10h ago
Just charge forward 👊
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PerpetualKing
· 10h ago
Just charge forward 👊
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PerpetualKing
· 10h ago
Just charge forward 👊
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