Google's TurboQuant algorithm, released at ICLR 2026, delivers a revolutionary KV-cache compression technique for LLMs: it compresses 16-bit precision down to 3 bits, cutting memory usage by 6x with zero precision loss. The technique uses orthogonal rotation and Lloyd-Max optimal quantization to tackle the memory bottleneck in long-context inference. On H100 GPUs, 4-bit TurboQuant speeds up attention computation by 8x, delivering significant inference cost optimization. This breakthrough not only shifts memory-chip market expectations, but also makes running long-context billion-parameter models on consumer hardware feasible — marking an important milestone in AI inference efficiency.