TurboQuant: Redefining AI efficiency with extreme compression
Google Research presents TurboQuant—a suite of theoretically grounded quantization algorithms that tackle the biggest memory bottlenecks in AI. By combining PolarQuant (which uses polar coordinates to eliminate memory overhead) and QJL (a 1‑bit error‑correction trick), TurboQuant compresses the key‑value cache to just 3 bits per value with zero accuracy loss.
In benchmarks, this delivers up to 8x faster attention computation on H100 GPUs and reduces memory footprint by 5–10x compared to standard 16/32‑bit formats—all without retraining.
The implications are huge: longer contexts, lower GPU requirements, and faster vector search at scale. A rare win where extreme compression meets provable efficiency.
