Comment by om8

https://docs.vllm.ai/en/v0.20.0/api/vllm/model_executor/laye...

`vllm.model_executor.layers.quantization.turboquant`

> The technique implemented here consists of the scalar case of the HIGGS quantization method (Malinovskii et al., "Pushing the Limits of Large Language Model Quantization via the Linearity Theorem", NAACL 2025; preprint arXiv:2411.17525): rotation + optimized grid + optional re-normalization, applied to KV cache compression. A first application of this approach to KV-cache compression is in "Cache Me If You Must: Adaptive Key-Value Quantization for Large Language Models" (Shutova et al., ICML 2025; preprint arXiv:2501.19392). Both these references pre-date the TurboQuant paper (Zandieh et al., ICLR 2026).