TurboQuant, KIVI, and the Real Cost of Long-Context KV Cache

I Built a Free KV Cache Calculator for LLM Inference When people talk about LLM deployment costs, they usually start with model weights. That makes sense, but once you push context length higher, KV cache becomes one of the real bottlenecks. In many long-context setups, it is the dynamic memory cost that quietly starts dominating deployment decisions. I built a small free tool to make that easier to estimate: TurboQuant Tools It is a practical KV cache calculator for LLM inference. You can use it to estimate memory for: MHA models GQA models MQA models different context lengths different batch sizes different KV cache precision settings I also added supporting pages for developers who want more context instead of just a calculator: TurboQuant explained TurboQuant vs KIVI KV cache primer ## Why I made it A lot of discussion around long-context inference stays too abstract. People know KV cache matters, but when you actually need to answer questions like these, the conversation often get