Google DeepMind released Gemma 4 on April 2, 2026 — four open-weight models under Apache 2.0 that run everywhere from a Raspberry Pi to a single H100 GPU. They support text, image, and audio inputs, 256K context windows, and native agentic workflows. Here’s everything you need to know.

The family at a glance

Model	Type	Params (effective)	Context	Modalities	Best for
Gemma 4 E2B	Edge MoE	2.3B (5.1B total)	128K	Text + Vision	Mobile, IoT, on-device
Gemma 4 E4B	Edge MoE	4.5B (8B total)	128K	Text + Vision + Audio	Edge devices, phones
Gemma 4 26B	MoE	3.8B active (26B total)	256K	Text + Vision	Best value — frontier quality at low cost
Gemma 4 31B	Dense	31B	256K	Text + Vision	Maximum quality, single GPU

All four models are released under Apache 2.0 — fully open for commercial use, fine-tuning, and redistribution. This is a first for the Gemma family.

What makes Gemma 4 different

Mixture of Experts done right

The 26B model is the standout. It has 26 billion total parameters but only activates 3.8 billion per forward pass. That means you get frontier-class reasoning at a fraction of the compute cost. In practice, it runs on hardware that would normally only handle a 4B model.

If you’ve used MiMo V2 Flash (which uses a similar MoE approach), the concept is familiar — but Gemma 4 pushes it further with better routing and lower active parameter counts.

Hybrid attention for long context

Gemma 4 uses a hybrid attention mechanism that alternates between local sliding window attention and full global attention. The final layer is always global, which maintains deep context awareness even in very long documents.

The edge models support 128K tokens. The larger models support 256K tokens — enough to process entire codebases or book-length documents in a single pass.

Multimodal from the ground up

Every Gemma 4 model handles text and images natively. The E4B edge model also supports audio input, making it suitable for voice-controlled applications on mobile devices.

This is a significant advantage over Qwen 3.5, which requires separate models for different modalities, and Llama 4, where multimodal support is limited to the larger variants.

Built for agents

All Gemma 4 models support function calling, structured JSON output, and native system instructions. Google specifically designed them for agentic workflows — multi-step reasoning tasks where the model plans, executes, and iterates.

Hardware requirements

Model	RAM (FP16)	RAM (Q4)	VRAM (FP16)	Runs on
E2B	5 GB	2 GB	5 GB	Raspberry Pi 5, phones
E4B	8 GB	4 GB	8 GB	Laptops, tablets
26B	26 GB	8 GB	26 GB	Gaming PC, Mac M2+
31B	62 GB	16 GB	62 GB	Single H100, Mac M3 Max

The E2B model is remarkably small. At Q4 quantization, it fits in 2 GB of RAM — making it one of the best AI models under 4GB RAM. You can genuinely run it on a Raspberry Pi.

The 26B MoE model is the sweet spot for most developers. Despite having 26B total parameters, its 3.8B active parameter count means it runs comfortably on a machine with 8 GB of RAM at Q4 quantization. That’s laptop-friendly.

For the 31B dense model, you’ll need more serious hardware. Check our GPU buying guide if you’re building a local AI rig.

How to run Gemma 4

The fastest way to get started is with Ollama:

# Install Ollama
curl -fsSL https://ollama.com/install.sh | sh

# Run the 26B MoE model (best value)
ollama run gemma4:26b

# Run the edge model (fastest)
ollama run gemma4:e2b

# Run the dense model (highest quality)
ollama run gemma4:31b

For more control over quantization and inference settings, use llama.cpp directly. See our detailed local setup guide for step-by-step instructions with llama.cpp, vLLM, and Docker.

Benchmarks

Gemma 4 26B punches well above its weight class:

Benchmark	Gemma 4 26B	Llama 4 Scout	Qwen 3.5 Plus	MiMo V2 Pro
MMLU	83.2	79.8	82.1	84.5
HumanEval (coding)	78.5	72.3	76.8	81.2
GSM8K (math)	89.1	85.4	87.3	90.8
Context handling	256K	10M	128K	1M
License	Apache 2.0	Llama License	Apache 2.0	Proprietary
Cost	Free	Free	Free/API	API only

The 26B model competes with models 5-10x its size on reasoning benchmarks. It doesn’t beat MiMo V2 Pro on raw quality, but MiMo V2 Pro is a proprietary API-only model costing $1-3 per million tokens. Gemma 4 is free.

Compared to Llama 4 Scout, Gemma 4 wins on coding and math benchmarks while using significantly less hardware. Llama 4’s advantage is its massive 10M token context window.

Which Gemma 4 model should you use?

Building a mobile app with AI? → Gemma 4 E2B or E4B. They’re designed for on-device inference with minimal battery impact.

Need a general-purpose local AI? → Gemma 4 26B. Best quality-per-compute ratio in the family. Runs on any modern laptop.

Want maximum quality on a single GPU? → Gemma 4 31B. Dense architecture means more predictable performance than MoE models.

Running AI on a Raspberry Pi or embedded device? → Gemma 4 E2B at Q4 quantization. It’s one of the best options for constrained hardware.

How Gemma 4 fits in the open model landscape

The open-source AI space in 2026 has three major players:

• Google Gemma 4 — Best for on-device and edge deployment. Smallest effective models with the best quality-per-parameter ratio.
• Meta Llama 4 — Best for massive context (10M tokens) and raw scale. The Maverick 400B model is the most powerful open model available.
• Alibaba Qwen 3.5 — Best for multilingual and coding tasks. Strong ecosystem with dedicated coding and math variants.

For a detailed comparison, see our Gemma 4 vs Llama 4 vs Qwen 3.5 breakdown.

If you’re choosing between open models and proprietary APIs, our self-hosted AI vs API comparison covers the tradeoffs in detail.

Getting started

1. Quickest path: Install Ollama and run ollama run gemma4:26b
2. More control: Follow our local setup guide for llama.cpp and vLLM
3. Compare options: Check our best local AI models by task ranking

Gemma 4 is the most accessible frontier-quality AI family released to date. The 26B MoE model running on a laptop delivers results that required a datacenter two years ago. If you haven’t tried running AI locally yet, this is the model to start with.