Tether Brings AI Memory Compression to Consumer Devices

Tether released TurboQuant, an open-source method that compresses LLM key-value cache during inference on consumer hardware.

The technique cuts memory use by roughly 3 to 6 times, with about 5x reductions in some cases, while leaving model weights unchanged.

It works on laptops and phones, making local AI more feasible for longer context and lower cloud dependence.

Prompt processing gets slower, but token generation remains close to full-precision speed.