Gemma 4 E2B

Name: Gemma 4 E2B
Author: Google

Google · Gemma 4 · released 2026-04-02 · data checked 2026-11-01

Gemma 4 E2B is a 5.1B-parameter dense model from Google (United States), released 2026-04-02 under the Apache-2.0 license. It scores 31.9 on the Small LM Index, #21 of 26 small models. It needs about 3.7 GB of memory at 4-bit with an 8K-token context.

2.3B effective parameters (5.1B including per-layer embeddings). Built for phones and browsers.

Small LM Index

31.9

#21 of 26 scored small models

Likely 28.2–35.9 · from 4 of 6 core benchmarks

Parameters

5.1B

2.3B effective

Price per 1M tokens

—

No hosted API on OpenRouter; self-host

Context window

128K

tokens

Benchmarks

Each bar shows this model's score. The tick marks the best small model on that benchmark. Sources: 6 reported by Google. How we label sources.

MMLU-Pro Lab-reported Knowledge60.0

Class best: Qwen3.5 9B 82.5

GPQA Diamond Lab-reported Knowledge43.4

Class best: Qwen3.5 9B 81.7

AIME Lab-reported Math · AIME 2026, no tools37.5

Class best: Apriel 1.6 15B Thinker 88.0

LiveCodeBench Lab-reported Coding · LiveCodeBench v644.0

Class best: Granite 4.2 8B 73.2

τ²-Bench Lab-reported Agentic24.5

Class best: Qwen3.5 9B 79.1

MMMU-Pro Lab-reported Vision44.2

Class best: Qwen3.5 9B 69.2

Not reported: estimated

What the index model expects from this model's other scores. Not a measurement.

Humanity's Last Exam≈ 3

SWE-bench Verified≈ 13

Specifications

Lab: Google (US · United States)
Architecture: Dense transformer
Total parameters: 5.1B
Active parameters: All (dense)
Size class: Small
Context window: 128K tokens
Input modalities: text, image, audio
Reasoning: Hybrid (thinking on/off)
License: Apache-2.0
Openness: Open weights
Memory to run: ~3.7 GB at 4-bit, 8K context (more sizes)
Hugging Face: 3.3M downloads (30 days) · 1.1K likes

Weights on Hugging Face ↗

Compare with

Gemma 4 E2B vs Gemma 3n E2B (5.4B, index 14.3)
Gemma 4 E2B vs Gemma 3 4B (4.3B, index 17.5)
Gemma 4 E2B vs Nanbeige4.2 3B (4.2B, index 70.4)
Gemma 4 E2B vs Spark-X2.6 4B (4.1B, index 67.5)

Sources

https://huggingface.co/google/gemma-4-31B-it

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Run it locally

Uses ggml-org/gemma-4-E2B-it-GGUF (Q4_0). Swap the tag for another size: BF16, Q4_0, Q8_0.

Ollama

ollama run hf.co/ggml-org/gemma-4-E2B-it-GGUF:Q4_0

llama.cpp

llama-server -hf ggml-org/gemma-4-E2B-it-GGUF:Q4_0

Then open http://localhost:8080 for a chat window.

MLX (Mac)

pip install -U mlx-lm
mlx_lm.chat --model mlx-community/gemma-4-e2b-it-4bit

Text only; for images use mlx-vlm.

vLLM (GPU server)

vllm serve google/gemma-4-E2B-it

Full-precision weights, for a server GPU.

Open in LM Studio

Memory needed: weights, context cache and runtime buffers.

Quantization	4K context	32K context	128K context
16-bit F16	11 GB	11 GB	11 GB
8-bit Q8_0	6.0 GB	6.1 GB	6.4 GB
4-bit Q4_K_M	3.7 GB	3.8 GB	4.1 GB

Check it against your GPU, Mac or phone →