TurboQuant trims KV cache, but results vary

My take: this is genuinely useful infrastructure, but the hype only holds if KV cache is your bottleneck.

• –It is a cache-compression method, not a model-architecture change, so in principle it can apply to other transformer KV caches if the implementation supports the tensor shapes and attention kernel.
• –The paper and Google blog emphasize Gemma and Mistral; I did not find a public evaluation on Qwen3.5-style cache behavior in the source material, so that part remains unverified.
• –Comparing “Q8” to TurboQuant is a bit apples-to-oranges unless you mean Q8 KV cache; TurboQuant is trying to beat standard low-bit cache quantization with less overhead and less accuracy loss.
• –The practical win is biggest for long context, large batch, or memory-constrained inference. If your workloads are short-context or already fit comfortably in RAM/VRAM, the benefit is much smaller.
• –For local LLM users, this is less “everything changes” and more “another strong tool in the long-context memory optimization stack.”