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TurboQuant and Traditional Quantization — Two Tools, Two Jobs | White Papers

AI & Machine Learning

TurboQuant and Traditional Quantization — Two Tools, Two Jobs

Learn how traditional quantization shrinks model weights while TurboQuant shrinks inference memory — and when to stack them.

Three Control Surfaces of AI Engineering: Prompts, Context, and Harness

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What you will learn Background The two bottlenecks in model serving Traditional quantization — shrinking the model Post-training quantization (PTQ)Quantization-aware training (QAT)What traditional quantization does not fix The KV cache — the other memory problem TurboQuant — compressing the notepad Random rotation Optimised scalar quantization Results The core difference When to use each Traditional quantization is right when When to skip traditional quantization TurboQuant is right when When to skip TurboQuant Stacking both Choosing an approach Current limitations Summary References

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