Open Source · Google · Tether · Crypto Briefing
Tether AI open-sources TurboQuant, reducing LLM KV cache memory apply by 5x
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The stablecoin giant's AI division adapts a Google Research algorithm into a production-ready tool that could make running large language models on phones and laptops feasible.
Key facts
- Instead of storing values as 16-bit or 32-bit floating point numbers, you compress them down to 4-bit or even 2-bit representations
- The release arrived as part of QVAC SDK version 0.12.0, which also includes new capabilities like text-to-video generation and robot control
- The algorithm behind TurboQuant originated from Google Research, which published the initial details on March 24, 2026
- A model that needs 16 GB of memory for its KV cache alone isn’t going to fit on most consumer devices
Summary
Tether AI released TurboQuant as open-source software, delivering a tool that compresses the memory footprint of large language model inference by up to five times. The algorithm behind TurboQuant originated from Google Research, which published the initial details on March 24, 2026. Quantization is a technique that reduces the precision of numbers used in neural network computations. The release arrived as part of QVAC SDK version 0.12.0, which also includes new capabilities like text-to-video generation and robot control.