Back in Q1, I read a headline about Google cutting AI memory use by 6x and filed TurboQuant under "watch and revisit" — no code, tested only up to 8B parameters, nothing to actually run against AI-NT-No-Problem. Four months is a long time in this industry. I went back to see what actually happened, and the honest answer is: a lot, but not the thing I expected.
What Google Actually Shipped
Quick recap for anyone who missed the original story. TurboQuant is a training-free algorithm suite — TurboQuant proper, plus PolarQuant and Quantized Johnson-Lindenstrauss — that compresses the KV cache specifically, not model weights, cutting memory by at least 6x with an 8x speedup in attention computation on H100s. The paper, "Online Vector Quantization with Near-optimal Distortion Rate," came out of Google Research and Google DeepMind and was accepted at ICLR 2026.
Here's the part that hasn't changed since March: as of the most recent status I could confirm, Google still hasn't shipped official code. The original "expected Q2 2026" timeline for an official release has quietly passed without one landing anywhere I can find.
The Ecosystem Filled the Vacuum, Then Fragmented
