Why it matters for an office
Quantization is what lets useful models run on office-sized hardware. It is also a hidden variable when comparing products, so ask which precision a vendor’s numbers used.
ANVE: the private AI office, on the record. See what that means
ANVEPrivate AI glossary · updated 2026-10-06
Quantization.
Defined in plain words, for offices deciding how to use AI.
Short answer. Quantization stores an AI model’s weights at lower numerical precision, for example 4 or 8 bits per weight instead of 16, so the model needs roughly a quarter to a half of the memory and usually runs faster. A 4-bit 27B model can fit in about 16 to 20 GB instead of over 50 GB. The cost is a small loss of accuracy that varies by model and task, which is why quantized models should be tested on the actual work.
How ANVE uses it
ANVE tests models at the precision they will ship with, and publishes the harness method alongside the results.
Questions people ask
- Is a 4-bit model much worse?
- Often only slightly for summaries and extraction; more for maths and code. Test on your own tasks.
What is not real yet
No sealed ANVE unit exists; every image of the appliance on this site is a labelled render. The software runs today on a development machine. No customers yet, no certifications, no delivery date, and the final price is not announced.
Reservations are ₹999 in India or US$12 elsewhere, refundable in full until you confirm an order in writing (terms). Offices that cannot use cloud AI can apply to be one of ten design partners.