# Gemma 3n E2B ![Organization](https://img.shields.io/badge/Org-Google-4285F4) ![Effective Params](https://img.shields.io/badge/Effective-2B-1f6feb) ![License](https://img.shields.io/badge/License-Gemma%20ToU-d29922) ![Weights](https://img.shields.io/badge/Weights-Open-2da44e) ![On--Device](https://img.shields.io/badge/Target-On--Device-2da44e) ![Released](https://img.shields.io/badge/Released-May%202025-555) > **Quick answer:** Gemma 3n E2B is Google's smallest May 2025 on-device multimodal model with 2B effective parameters, scoring 51.7% on ARC-C, 40.5% on MMLU-Pro, and 72.2% on HellaSwag. It is designed for highly constrained on-device deployment with text, image, and audio input support. ## At a Glance **Where Gemma 3n E2B leads** - Smallest Gemma 3n model — lowest inference cost - Multimodal: text, image, and audio inputs at 2B effective param scale - MatFormer nested architecture for efficient on-device execution - Suitable for most-constrained mobile hardware **Where it lags** - 40.5% MMLU-Pro — limited academic knowledge at 2B scale - Below Gemma 3n E4B on all benchmarks (expected at 2B vs 4B) - Text-only alternatives (Llama 3.2 1B) offer similar size **Best for:** Most-constrained on-device deployment; wearables and IoT; minimum-footprint multimodal AI. ## What Gemma 3n E2B Is Gemma 3n E2B is the smallest model in Google's Gemma 3n family, announced at Google I/O 2025. At 2B effective parameters with the MatFormer nested architecture, it targets the most resource-constrained on-device use cases — wearables, IoT, and entry-level smartphones. Like Gemma 3n E4B, it supports text, image, and audio inputs — multimodal capability at scales where most models are text-only. The MatFormer architecture allows dynamic compute allocation, so actual inference cost varies by task complexity. For most mobile applications, Gemma 3n E4B provides meaningfully better benchmarks (50.6% vs 40.5% MMLU-Pro) with acceptable additional compute. E2B is preferred when E4B exceeds device constraints. ## Specifications | Field | Value | |---|---| | Organization | Google | | Effective parameters | 2B (MatFormer nested) | | License | Gemma Terms of Use | | HuggingFace | [google/gemma-3n-E2B-it](https://huggingface.co/google/gemma-3n-E2B-it) | | Release date | May 20, 2025 | | Knowledge cutoff | February 2025 | | Modality | Text + Vision + Audio | | Architecture | MatFormer (nested) | ## Pricing Open weights under Gemma Terms of Use — free to self-host. Optimised for on-device deployment. ## Public Benchmark Scores | Benchmark | Score | Source | Date | |---|---|---|---| | [ARC-C](/benchmarks/ai2-reasoning-challenge) | 51.7% | Benchgen evaluation | 2025-07 | | [HellaSwag](/benchmarks/hellaswag) | 72.2% | Benchgen evaluation | 2025-07 | | [MMLU-Pro](/benchmarks/mmlu-pro) | 40.5% | Benchgen evaluation | 2025-07 | ## Gemma 3n E2B vs Alternatives | Model | MMLU-Pro | Modality | Effective Params | |---|---|---|---| | **Gemma 3n E2B** | 40.5% | Text+Vision+Audio | 2B | | [Gemma 3n E4B](/models/google/gemma-3n-e4b) | 50.6% | Text+Vision+Audio | 4B | | [Llama 3.2 3B Instruct](/models/meta/llama-3-2-3b-instruct) | — | Text only | 3B | Gemma 3n E4B provides +10pp MMLU-Pro at 2× the effective parameters. Use E2B only when E4B won't fit on the target device. ## Related Models & Benchmarks - Compare: [Gemma 3n E4B](/models/google/gemma-3n-e4b) · [Gemma 3 12B](/models/google/gemma-3-12b) · [Llama 3.2 3B Instruct](/models/meta/llama-3-2-3b-instruct) - Benchmarks: [MMLU-Pro](/benchmarks/mmlu-pro) · [HellaSwag](/benchmarks/hellaswag) ## Frequently Asked Questions

What is Gemma 3n E2B?

Google's May 2025 smallest on-device multimodal model with 2B effective parameters (MatFormer), scoring 51.7% ARC-C, 40.5% MMLU-Pro, and 72.2% HellaSwag. Supports text, image, and audio.

Should I use Gemma 3n E2B or E4B?

E4B for better performance (50.6% vs 40.5% MMLU-Pro). Use E2B only when E4B exceeds device hardware constraints.

--- *Specs from Google's official Gemma 3n release (May 2025) and Benchgen evaluations. Last updated 2026-07-24.*

Gemma 3n E2B Instructed — Evaluations

Evaluations