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AI Glossary

What is Model Quantisation?

Insta's plain English

Shrinking AI models to run faster and cheaper without losing much accuracy or performance.

A compression technique that reduces AI model file sizes by using simpler number formats, making models faster and cheaper to run.

The full picture

Model quantisation works like compressing a high-resolution photo into a smaller file size. AI models store millions of numbers that represent what they've learned. Normally these numbers are very precise (like measuring to the thousandth of an inch), but quantisation rounds them to simpler values (like measuring to the nearest half-inch). This dramatically reduces the model's file size—sometimes by 75% or more—while keeping performance nearly identical.

For businesses, this matters because smaller models mean lower costs and better performance. You can run AI on cheaper hardware, process requests faster, serve more customers simultaneously, and even run AI directly on phones or tablets instead of expensive cloud servers. Companies save thousands monthly on infrastructure costs while delivering snappier experiences to customers.

You don't need to understand the technical details, but you should know that quantisation is now standard practice for deploying AI efficiently. When evaluating AI solutions, ask vendors if their models are quantised and what performance trade-offs exist. Most modern AI tools use quantisation automatically behind the scenes, giving you better value without requiring any extra effort on your part.

📌 Real business example

A retail company running a customer service chatbot quantises their AI model to reduce cloud computing costs by 60%. Instead of paying for expensive GPU servers, they run the compressed model on standard machines, handling 10,000 daily customer conversations at a fraction of the original cost while maintaining the same response quality customers expect.

How different roles use this

Marketer
Deploy AI-powered personalization tools on your website that load instantly and don't slow down page speed, keeping bounce rates low while delivering smart product recommendations to every visitor.
Business owner
Cut your AI infrastructure costs by 50-75% by using quantised models that deliver the same customer experience on cheaper hardware, directly improving your profit margins.
Executive
Evaluate AI vendors by asking about quantisation capabilities to ensure you're getting cost-efficient solutions that scale affordably as your business grows without ballooning cloud bills.

Common questions

Q: Does quantisation make AI less accurate?
There's typically a small accuracy decrease (1-2%), but it's usually imperceptible to end users and far outweighed by the cost savings and speed improvements you gain.
Q: Do I need to do anything special to use quantised models?
No, most modern AI services handle quantisation automatically. When building custom solutions, simply ask your vendor or development team to implement it—it's now a standard best practice.
Q: How much money can quantisation actually save my business?
Businesses typically see 50-75% reductions in AI infrastructure costs. A company spending $10,000 monthly on AI cloud services might reduce that to $2,500-$5,000 with properly quantised models.

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