Gemma 4
Gemma 4
Released March 31, 2026 by Google DeepMind. Apache 2.0 licensed. Multimodal (text + image, audio on small models).
Model Sizes
| Model | Type | Effective Params | Context | Modalities |
|---|---|---|---|---|
| E2B | Dense | 2.3B (5.1B w/ embeddings) | 128K | Text, Image, Audio |
| E4B | Dense | 4.5B (8B w/ embeddings) | 128K | Text, Image, Audio |
| 26B A4B | MoE | 3.8B active / 25.2B total | 256K | Text, Image |
| 31B | Dense | 30.7B | 256K | Text, Image |
The 26B A4B is the standout — a MoE model that runs almost as fast as a 4B model despite 26B total params. The E2B/E4B use Per-Layer Embeddings for on-device efficiency.
Local Running Options
- Ollama —
ollama run google/gemma-4(all sizes). Easiest one-command setup. - llama.cpp — GGUF quantized versions available on Hugging Face. Good for CPU/GPU hybrid inference.
- vLLM — For higher-throughput server deployment. Supports the native HF safetensor weights.
- LM Studio — GUI-based, supports GGUF formats. Good for desktop use.
- Hugging Face Transformers — Direct Python API. Full precision or QLoRA fine-tuning.
Hardware Requirements (rough)
- E2B (2.3B eff.) — Runs on phones, any modern laptop (4-8 GB RAM)
- E4B (4.5B eff.) — 8-16 GB RAM, most 2024+ MacBooks
- 26B A4B — 16-24 GB VRAM (single GPU), or CPU with enough RAM
- 31B — 24-48 GB VRAM (A100/H100 recommended), or multi-GPU
The 26B A4B is generally considered the sweet spot for local use — frontier-level benchmarks (82.6 MMLU Pro, 88.3 AIME) with ~4B active parameter compute cost.
All models are on Hugging Face under google/gemma-4-*.