AI Glossary · Data & Platform
Quantization
Reducing model precision (e.g., 16-bit to 4-bit) to shrink memory and cost with minimal quality loss — key to on-prem and edge deployment.
Definition
What is Quantization?
Quantization is reducing model precision (e.g., 16-bit to 4-bit) to shrink memory and cost with minimal quality loss — key to on-prem and edge deployment.
- Category
- Data & Platform
- Glossary set
- 14 related terms
- Audience
- Enterprise AI leaders
Why does Quantization matter in enterprise AI?
Quantization matters in enterprise AI platforms because it affects data readiness, governance, scalability, and the operating foundation for AI use cases.
Related terms in Data & Platform
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- Bring Your Own Model (BYOM)
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- Data Fabric
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- Data Lakehouse
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- Embedding
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- Feature Store
- A managed catalog of reusable ML features with online and offline serving — key to consistent training and inference.