TurboQuant compatibility questioned on MLA models

The big question is not whether TurboQuant is impressive on paper, but how much room it still has to help once MLA has already reduced cache pressure. My read is that the gains may still be useful, but the result will depend heavily on kernel support and whether the model’s attention layout leaves enough headroom to matter.

• –Google’s TurboQuant claims are strong for KV-cache compression in benchmarked stacks, but the public results center on Gemma and Mistral, not MLA models like GLM-4.7-Flash.
• –MLA already shrinks the cache footprint, so TurboQuant may face diminishing returns or shift the bottleneck from memory to compute and integration overhead.
• –Implementation details matter here: rotation, quantization, and special-case attention paths can erase theoretical wins if the backend is not tuned for the model shape.
• –The right way to judge it is end-to-end serving metrics: peak memory, tokens/sec, long-context quality, and whether the added complexity is worth the incremental savings.