SIDE BY SIDE

gemma-3-12b-itvsgemma-3-4b-it

Two language models, with their source-backed specifications in one place.

Choose different models →

4-bit weight storage

A shorter bar means less theoretical space for weights. Extra runtime memory is required.

gemma-3-12b-it6 GB
gemma-3-4b-it2 GB

Published context limit

A longer bar means more advertised context capacity, not better recall or higher intelligence.

gemma-3-12b-itNot reported
gemma-3-4b-itNot reported

What can we conclude?

gemma-3-4b-it has the smaller theoretical weight footprint, which leaves more of a fixed memory budget for other work. An overall quality or speed winner needs comparable benchmark evidence; that evidence is not connected here yet.

Detailed specifications and missing evidence
What mattersgemma-3-12b-itgemma-3-4b-it
MakerGoogleGoogle
Model typeLanguageLanguage
CapabilitiesVisionVision
Inputs → outputstext, image → texttext, image → text
AccessDownloadable weightsDownloadable weights
StatusAvailableAvailable
Parameters12BModel name (nominal size)4BModel name (nominal size)
4-bit weights only≈ 6 GB≈ 2 GB
8-bit weights only≈ 12 GB≈ 4 GB
16-bit weights only≈ 24 GB≈ 8 GB
Published contextNot reportedNot reported
Licensegemmagemma
Access approvalMaker approval or terms requiredMaker approval or terms required
Source listing dateMar 1, 2025Feb 20, 2025
Date meaningRepository created; may precede public releaseRepository created; may precede public release
Last checkedSep 10, 2026Sep 10, 2026
Cloud pricingNot connectedNot connected
Comparable benchmark scoreNo verified result availableNo verified result available
Tokens per secondNo verified measurement availableNo verified measurement available

Check the evidence before choosing.

Estimates use total parameters × bits ÷ 8, in decimal GB. They exclude conversation cache, activations, runtime memory, and quantization overhead. A compatible quantized version is not guaranteed. Source listing dates are not verified release dates.

gemma-3-12b-it source ↗ · gemma-3-4b-it source ↗

Explore models by VRAM budget · Read the comparison methodology