Is Turboquant really a game changer?

Reddit r/LocalLLaMA / 4/5/2026

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Key Points

The post discusses whether TurboQuant is truly a major improvement for local LLMs by focusing on how quantizing the KV cache affects quality and memory usage.
The author compares Gemma 4’s reported 2x RAM needs at the same context length against expectations that higher KV cache precision (e.g., Q8) might already preserve context sufficiently.
A key question raised is whether TurboQuant’s benefits apply to Qwen’s specific KV cache architecture, noting that Qwen’s architecture may not have been evaluated with TurboQuant in its published work.
The overall context is the author’s early learning of deploying LLMs locally and trying to understand the practical tradeoffs among model RAM, KV cache precision, and quantization-induced losses.

I am currently utilizing qwen3.5 and Gemma 4 model.

Realized Gemma 4 requires 2x ram for same context length.

As far as I understand, what turbo quant gives is quantizing kv cache into about 4 bit and minimize the loses

But Q8 still not lose the context that much so isn't kv cache ram for qwen 3.5 q8 and Gemma 4 truboquant is the same?

Is turboquant also applicable in qwen's cache architecture? because as far as I know they didn't tested it in qwen3.5 style kv cache in their paper.

Just curious, I started to learn local LLM recently