Gemma 3n E4B
Gemma 3n is a multimodal model designed to run locally on hardware, supporting image, text, audio, and video inputs. It features a language decoder, audio encoder, and vision encoder, and is available in two sizes: E2B and E4B. The model is optimized for memory efficiency, allowing it to run on devices with limited GPU RAM. Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. Gemma models are well-suited for a variety of content understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. Gemma 3n models are designed for efficient execution on low-resource devices. They are capable of multimodal input, handling text, image, video, and audio input, and generating text outputs, with open weights for instruction-tuned variants. These models were trained with data in over 140 spoken languages.
Identité
| Organisation | |
|---|---|
| Pays | — |
| Famille | — |
| Type | llm |
| Date de sortie | 2025-06-26 |
| Licence | proprietary |
| Licence commerciale | non |
| Licence open source | non |
| Poids ouverts | non |
| Auto-hébergement (DGX Spark) | non — poids fermés |
| Modalités | — |
| Fenêtre de contexte | — |
| KV cache / token (Q8) | — |
| Paramètres totaux | 8 B |
| Paramètres actifs (session) | 4 B |
| Source des paramètres actifs | web : https://ai.google.dev/gemma/docs/gemma-3n |
| Curation des paramètres actifs | 2026-10-19 |
| Prix par session | — |
| Tokens / tâche (Data Players) | 1.2 M |
| Tokens / tâche (Terminal-Bench) | 83.3 M (est.) |
| Ratio Terminal-Bench / Data Players | 69.4× |
| Coût / tâche (Terminal-Bench) | — |
| Tokens d'entraînement | 11 000 000 000 000 |
| Coupure de connaissance | 2024-06-01 |
| Créé le | 2026-05-07T16:53:29.950284Z |
| Mis à jour le | 2026-11-04T06:04:46.198839Z |
| Source | LLM Stats |
| URL | https://llm-stats.com/models/gemma-3n-e4b |
Prix par fournisseur
Toutes sources confondues : catalogue llm-stats, routeurs EUrouter/Cortecs et catalogues directs (OVHcloud, Scaleway, Infomaniak). Les prix sont convertis en USD pour la comparaison ; le prix publié et le taux BCE utilisé sont dans l'infobulle. Les prix inconnus restent indiqués « — ».
Aucune donnée.
Souveraineté qualifiée par offre (modèle × fournisseur) ; survolez le badge pour les critères.
SouverainetéScores par catégorie (top_scores)
| Catégorie | Score |
|---|---|
| language | 0.70 |
| math | 0.60 |
| physics | 0.80 |
| psychology | 0.50 |
| reasoning | 0.60 |
| search | 0.20 |
| general | 0.60 |
| creativity | 0.50 |
Scores par benchmark
| Benchmark | Catégorie | Score | Max | Normalisé (API) | Vérifié | Auto-déclaré | Daté du |
|---|---|---|---|---|---|---|---|
| arc-c | reasoning | 0.62 | 1.00 | 0.62 | non | oui | 2026-11-04T06:04:46.203285Z |
| arc-e | reasoning | 0.82 | 1.00 | 0.82 | non | oui | 2026-11-04T06:04:46.213174Z |
| big-bench-hard | reasoning | 0.53 | 1.00 | 0.53 | non | oui | 2026-11-04T06:04:46.224383Z |
| boolq | language | 0.82 | 1.00 | 0.82 | non | oui | 2026-11-04T06:04:46.228517Z |
| drop | reasoning | 0.61 | 1.00 | 0.61 | non | oui | 2026-11-04T06:04:46.233315Z |
| hellaswag | reasoning | 0.79 | 1.00 | 0.79 | non | oui | 2026-11-04T06:04:46.236495Z |
| natural-questions | reasoning | 0.21 | 1.00 | 0.21 | non | oui | 2026-11-04T06:04:46.241233Z |
| piqa | reasoning | 0.81 | 1.00 | 0.81 | non | oui | 2026-11-04T06:04:46.245855Z |
| social-iqa | reasoning | 0.50 | 1.00 | 0.50 | non | oui | 2026-11-04T06:04:46.253401Z |
| triviaqa | general | 0.70 | 1.00 | 0.70 | non | oui | 2026-11-04T06:04:46.257861Z |
| winogrande | reasoning | 0.72 | 1.00 | 0.72 | non | oui | 2026-11-04T06:04:46.261689Z |
Classements
Aucune donnée.
Données
Dates de curation (récupération depuis l'API) : models 2026-11-04 · benchmarks 2026-11-04 · scores 2026-11-04 · updates 2026-11-04 · account 2026-11-04 · model_history 2026-11-04 · rankings 2026-11-04