Tether has introduced TurboQuant, an open-source AI memory compression algorithm designed for consumer devices such as laptops and smartphones. This technology compresses the key-value (KV) cache of large language models (LLMs) by 3-6 times, enabling local AI applications to function more efficiently without relying on cloud resources. TurboQuant achieves this compression during inference sessions without altering the trained model weights, thus preserving output quality. While it does introduce a performance tradeoff in prompt processing speed—potentially reducing throughput to 30-60% of baseline—the overall accuracy remains comparable to uncompressed models, with only a -0.03% change in perplexity. By allowing local AI to handle larger workloads and longer interactions, TurboQuant opens new avenues for startups to implement AI solutions affordably while safeguarding proprietary data from cloud exposure. This advancement represents a significant step towards making powerful AI tools accessible on consumer-level devices.