Google Just Solved One of AI’s Biggest Hidden Bottlenecks — and Most People Missed It
Google Just Solved One of AI's Biggest Hidden Bottlenecks Every time an LLM generates a response, it quietly runs one of the most memory-hungry operations in computing — the KV cache . As conversations get longer and models get bigger, it becomes a wall. Expensive. Slow. Stubborn. Google Research just published a paper that hits that wall with a sledgehammer. The Short Version Traditional quantization methods save space on data, then spend it right back storing bookkeeping constants. You compressed the model, then padded it back out again. 🤦 Google's answer — TurboQuant — is a trio of algorithms that eliminates the overhead entirely, not by compressing it, but by redesigning the geometry so it was never needed in the first place. The results: KV cache down to 3 bits — no retraining required Memory footprint reduced by 6x Up to 8x speedup on H100 GPUs Long-context benchmark accuracy? Essentially unchanged And it outperformed methods hand-tuned to specific datase...