TurboQuant: How Google Compressed AI Memory by 6x Without Losing a Single Answer
How Google compressed KV cache memory by 6x using random rotations and Johnson-Lindenstrauss projections — without losing accuracy.
intermediate~4 hours3 notebooksQuantization FundamentalsRandom RotationPolarQuantQJL Error CorrectionTurboQuant Pipeline
Curator of this Module
Dr. Rajat Dandekar
Course Instructor
Dr. Rajat Dandekar is a researcher and educator specializing in AI/ML, with a passion for making complex concepts accessible through intuitive explanations and hands-on learning.
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