Title: Gemma 4 E4B IT vs Gemma 4 12B It: Parametri, Li%C4%8Benzja %E2%80%94 LLM EU
Canonical: https://llmeu.com/mt/compare/gemma-4-e4b-it-vs-gemma-4-12b-it/
Markdown mirror: https://llmeu.com/mt/compare/gemma-4-e4b-it-vs-gemma-4-12b-it.md
Machine-readable: https://llmeu.com/llms.txt (index) - https://llmeu.com/llms-full.txt (full catalogue) - https://llmeu.com/llms-catalogue.json

# Gemma 4 E4B IT vs Gemma 4 12B It

Qieg%C4%A7ed %C5%BCew%C4%A1 mudelli sa erbg%C4%A7a ma' xulxin fuq l-oqsma li jidde%C4%8Biedu deployment: kuntest, prezz, re%C4%A1jun, li%C4%8Benzja, lingwi, hosting, u x'hu verifikat.

Fejn %C5%BCew%C4%A1 mudelli jvarjaw, din it-tabella turi d-differenza u tieqaf. LLM EU ma tippubblikax badge ta' rebbie%C4%A7 ming%C4%A7ajr punte%C4%A1%C4%A1 dokumentat g%C4%A7al dik il-kompitu, u figura nieqsa tibqa' nieqsa minflok ma ssir %C5%BCero.

## Meta tu%C5%BCah

### [Gemma 4 E4B IT](/mt/models/gemma-4-e4b-it/)

#### Sa%C4%A7%C4%A7iet

- native audio input in the small-model class
- Apache-2.0 licence
- runs on modest hardware
- text, image and audio in one checkpoint

#### Limiti

- reasoning scores well below the larger Gemma 4 sizes
- 128K-token window, half of the medium models
- no evaluation has been run by LLM EU

#### Kompiti

[Spi%C5%BCa baxxa](/mt/tasks/cheap/), [Vi%C5%BCjoni](/mt/tasks/vision/), [Appo%C4%A1%C4%A1 multilingwi](/mt/tasks/multilingual-support/), [Sommarizzazzjoni](/mt/tasks/summarization/)

### [Gemma 4 12B It](/mt/models/gemma-4-12b-it/)

#### Sa%C4%A7%C4%A7iet

- open weights are published on Hugging Face and can be downloaded without a vendor agreement
- Apache-2.0 licence, so commercial self-hosting is permitted without a separate agreement
- at 12B parameters it fits on a single accelerator
- instruction-tuned checkpoint with a chat template in the repository
- speech recognition runs locally, so audio never leaves the operator premises

#### Limiti

- catalogue only %E2%80%94 LLM EU does not run this model, and nothing on this card implies an LLM EU endpoint
- no evaluation has been run by LLM EU
- the published configuration records no context window, so the card carries none
- at 12B parameters, self-hosting needs a single accelerator with enough memory for the weights
- vision checkpoints are heavier to serve than their parameter count alone suggests, because the image encoder and pre-processing run alongside the language model

#### Kompiti

[Appo%C4%A1%C4%A1 multilingwi](/mt/tasks/multilingual-support/), [Vi%C5%BCjoni](/mt/tasks/vision/), [Sommarizzazzjoni](/mt/tasks/summarization/)

**Kompiti:** [Vi%C5%BCjoni](/mt/tasks/vision/), [Appo%C4%A1%C4%A1 multilingwi](/mt/tasks/multilingual-support/), [Sommarizzazzjoni](/mt/tasks/summarization/)

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