Gemma 4: What It Is, What It Can Do, and Who Should Use It

Google DeepMind released Gemma 4 as its most capable open model family to date — built directly from the research behind the Gemini 3 series. If you’re evaluating open-weight models for a project, this guide gives you the factual rundown: what the model does, how the architecture works, where it fits, and where it doesn’t.

No benchmarked claims are invented here. Everything below is sourced from the official Gemma 4 model card and the Google DeepMind model page.

Quick Answer: What Is Gemma 4?

Gemma 4 is a family of open-weight, multimodal language models from Google DeepMind released in 2026. It comes in four sizes — E2B, E4B, 26B A4B (MoE), and 31B (Dense) — and handles text, image, video, and audio input while generating text output.

Key facts at a glance:

• License: Apache 2.0
• Architectures: Dense (E2B, E4B, 31B) and Mixture-of-Experts (26B A4B)
• Context window: 128K tokens (E2B/E4B), 256K tokens (26B A4B / 31B)
• Languages: Trained on 140+ languages; out-of-the-box multilingual for 35+ languages
• Thinking mode: Configurable reasoning mode across all sizes
• Download: Hugging Face, Kaggle, Ollama, LM Studio, Docker

The Four Model Sizes Explained

Understanding the naming convention prevents confusion before you deploy anything.

E2B and E4B — Designed for Edge and Mobile

The “E” stands for effective parameters. These smaller models use Per-Layer Embeddings (PLE), which give each decoder layer its own token embedding. The embedding tables are large in raw storage, but because they are accessed via fast lookup rather than full computation, the effective parameter count is much lower. This makes them suitable for phones, Raspberry Pi, Jetson Nano, and browser deployment.

• Context window: 128K tokens
• Native audio support (Automatic Speech Recognition, speech-to-text translation)
• Designed for offline, near-zero latency inference
• Runnable in Google AI Edge Gallery

26B A4B — Mixture-of-Experts for Speed

The “A” stands for active parameters. The model contains 26 billion total weights, but only activates 4 billion per token during inference. This Mixture-of-Experts (MoE) approach means inference runs at approximately the speed of a 4B model while drawing on 26B of trained knowledge. All 26B parameters must still be loaded into memory, so the GPU footprint remains closer to a 26B model.

• Context window: 256K tokens
• Optimal for high-throughput, cost-sensitive API deployments
• Better speed-per-quality ratio than the 31B Dense model for most workloads

31B Dense — Full-Precision Local Server

The 31B Dense model is the largest and most capable single-model variant in the Gemma 4 family. All parameters are active on every token. It targets high-end workstations, research environments, and on-premises deployments that require frontier-level output without relying on cloud APIs.

• Context window: 256K tokens
• Best absolute quality among the Gemma 4 series
• Requires more VRAM than the comparable MoE model

Key Capabilities

Based on the official model card, Gemma 4 models support the following across the entire size range unless otherwise noted.

Configurable Thinking Mode

All Gemma 4 models support a reasoning mode where the model produces internal step-by-step deliberation before generating its final answer. Thinking is toggled via the <|think|> token in the system prompt — include it to enable, remove it to disable.

When enabled, the model outputs a structured thought block followed by the final response. In multi-turn conversations, prior thought blocks are stripped from history — only the final answers are retained between turns.

Multimodal Input

All sizes handle text and image input natively, with support for variable aspect ratios and resolutions. Image resolution is controlled through a configurable visual token budget (70, 140, 280, 560, or 1120 tokens), which lets you trade off detail against inference cost.

• E2B and E4B only: native audio input — Automatic Speech Recognition (ASR) across multiple languages
• All sizes: video understanding via frame sequence processing
• All sizes: interleaved multimodal input (text and images mixed freely in a single prompt)

Native Function Calling

Gemma 4 introduces built-in support for structured tool use — also called function calling. This is the foundation for agentic workflows where the model plans, selects tools, and executes multi-step tasks. Native support does not require custom workarounds or external prompt engineering frameworks.

Native System Prompt Support

Previous Gemma versions required specific workarounds to implement system-level instructions. Gemma 4 natively supports the system role alongside user and assistant roles, making it straightforward to define behavior, personas, and constraints in production applications.

Coding and Reasoning

The model card notes notable improvements in coding benchmarks across the Gemma 4 family. All models support code generation, completion, and correction. The thinking mode is particularly relevant for multi-step coding tasks requiring debugging or decomposition.

Multilingual

Gemma 4 was pre-trained on data in 140+ languages and demonstrates out-of-the-box conversational and reasoning ability in 35+ languages. This is not just machine translation — the multilingual support extends to cultural context understanding per the official capability description.

Architecture: What Makes Gemma 4 Different from Gemma 3

Gemma 4 uses a hybrid attention mechanism that interleaves local sliding window attention with full global attention. The final layer is always a global attention layer. This design delivers the memory efficiency of lightweight attention without sacrificing awareness of long-range dependencies in extended contexts.

For long contexts specifically:

• Global layers use unified Key-Value caches to reduce memory overhead
• Proportional RoPE (p-RoPE) is applied to positional encoding to support extended sequences

The MoE architecture in the 26B A4B model activates only a subset of weights per token during inference, enabling high-throughput deployments without proportionally scaling compute requirements.

Where Gemma 4 Fits: Practical Use Cases

These are grounded use cases based on the model’s documented capabilities. If you’re thinking about where AI fits in your broader affiliate or content workflow, see our guide on how to start affiliate marketing without getting overwhelmed.

1. Local Code Assistant (26B A4B or 31B)

Developers running local-first AI setups can use the 26B A4B or 31B model via Ollama or LM Studio on a consumer GPU. With a 256K context window, you can pass large codebases and get contextually accurate completions, debugging assistance, and architectural suggestions without sending code to cloud services.

2. On-Device Mobile Applications (E2B or E4B)

The E2B and E4B models are purpose-built for edge deployment. With native audio support and offline inference capability, they suit applications like transcription tools, voice-driven assistants, or privacy-first mobile apps where server round trips are unacceptable.

3. Multilingual Content Analysis (All Sizes)

With 140+ pre-training languages, Gemma 4 handles document analysis, classification, and summarization across language boundaries. This is relevant for international publishing, multilingual customer support automation, and localization review workflows.

4. Agentic Research Tools (26B A4B or 31B)

With native function calling, a 256K context window, and configurable thinking, the larger Gemma 4 models power autonomous agents that can plan tasks, call external tools, and reason across long documents — all without proprietary API lock-in.

5. RAG and Document Intelligence (Any Size)

The long context windows (128K for small, 256K for medium/large) reduce the engineering complexity of retrieval-augmented generation systems. Entire documents or extensive conversation histories can be passed in context, reducing retrieval precision requirements.

Strengths

Based only on what is documented in official sources:

• Deployment flexibility: Four sizes covering mobile through server-grade hardware
• Apache 2.0 license: Commercial use permitted with attribution
• No cloud dependency: Models are fully downloadable and runnable locally
• Thinking mode: Configurable reasoning without a separate model variant
• Long context: 256K tokens for the medium/large sizes — useful for complex analysis tasks
• Multimodal coverage: Text, image (all), video (all), audio (E2B/E4B) in a single model family
• Native agentic support: Function calling and system prompts without framework workarounds
• Ecosystem support: JAX, Keras, PyTorch, Hugging Face Transformers, Vertex AI, GKE, Cloud Run, Ollama, LM Studio

Trade-offs and Limitations

This section covers what the official documentation indicates are design trade-offs, not deficiencies.

Memory Requirements Don’t Always Match the Label

The E2B and E4B models require more GPU or TPU memory than their parameter count implies because the Per-Layer Embedding tables — while used only for fast lookup — must still be loaded. The 26B A4B model similarly requires memory closer to a 26B dense model even though inference runs at 4B-equivalent speed.

Implication: Verify actual memory requirements in the inference memory table before hardware selection. Do not size hardware based on the parameter count alone.

Audio Only on Small Models

Audio input (ASR, speech-to-text translation) is natively supported on E2B and E4B only. The 26B A4B and 31B models handle text and image input, plus video via frames, but not direct audio.

Context Window Tiers

The 128K / 256K split means the small on-device models have less room for long-document tasks compared to the medium/large models. For tasks requiring extensive multi-document reasoning, the 26B A4B or 31B models are the appropriate choice.

Thinking Mode Behavior Varies by Size

For the 26B A4B and 31B models, even when thinking is disabled, the model still outputs the thought block tags (just with an empty content block). This differs from E2B and E4B behavior. Libraries like Transformers and llama.cpp handle this automatically, but custom inference implementations need to account for it.

Fine-Tuning Overhead

Fine-tuning memory requirements are substantially higher than inference requirements. Full-precision fine-tuning of the 31B model is out of reach on consumer hardware. Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA reduce this significantly and are supported via Keras, Hugging Face Transformers, and the Gemma library.

Deployment Options at a Glance

Model weights are available from Hugging Face (google/gemma-4 collections), Kaggle, Ollama registry, LM Studio catalog, and Docker Hub.

Pre-launch Checklist Before Deploying Gemma 4

• Confirm target hardware meets actual memory requirements (not just parameter count estimate)
• Choose the right size: E2B/E4B for edge, 26B A4B for throughput, 31B for quality
• Review Apache 2.0 license terms and the Gemma Prohibited Use Policy
• Set the recommended sampling parameters: temperature=1.0, top_p=0.95, top_k=64
• Handle the thinking mode token (<|think|>) explicitly — do not assume default behavior
• For multimodal inputs, place image/audio before text in the prompt
• For fine-tuning, account for drastically higher memory overhead vs. inference
• Test multi-turn conversation behavior — prior thinking tokens must not be included in history

Frequently Asked Questions

Is Gemma 4 free to use commercially? Gemma 4 is released under the Apache 2.0 license, which permits commercial use. You should also review the Gemma Terms of Use and the Intended Use Statement before deployment.

Can I run Gemma 4 on a consumer GPU? The E2B and E4B models are designed to run on consumer hardware and mobile devices. The 26B A4B model can run on a high-end consumer GPU (e.g., RTX 4090 at 24GB VRAM) depending on quantization level. The 31B Dense model requires either a high-end GPU with sufficient VRAM or multi-GPU setups. Check the memory requirements table for specifics by quantization level.

What is the difference between Gemma 4 and Gemma 3? Gemma 4 introduces several architectural and capability changes over Gemma 3: hybrid attention with local/global interleaving, the Per-Layer Embedding approach for edge models, native system role support, native function calling, configurable thinking mode, extended context windows (up to 256K), and a new MoE size (26B A4B). The audio capability on small models is also new to this generation.

Can Gemma 4 process video? Yes. All Gemma 4 sizes support video understanding by processing sequences of frames extracted from video content.

Is thinking mode always on by default? No. Thinking mode is opt-in. Include the <|think|> token in your system prompt to enable it. Remove it to run in standard generation mode.

Where can I try Gemma 4 without setting up local infrastructure? Google AI Studio provides access to Gemma 4 models via a browser interface. The 31B instruction-tuned variant is directly accessible here.

Does Gemma 4 support fine-tuning? Yes. Fine-tuning is supported via Keras (LoRA), Hugging Face Transformers (QLoRA, full fine-tune), the Gemma library, and distributed tuning approaches. The memory overhead for fine-tuning is significantly higher than for inference — plan accordingly.

Summary

Gemma 4 is a well-structured open model family covering a meaningful range of deployment scenarios: from offline mobile inference at the E2B/E4B end to high-throughput cloud serving at the 26B A4B end, with a 31B Dense option for maximum quality. The Apache 2.0 license, native function calling, configurable thinking mode, and extended context windows make it a practical choice for teams building production AI applications without cloud API dependencies.

The trade-offs are real — memory requirements don’t scale as intuitively as the parameter names suggest, audio is limited to small models, and fine-tuning overhead is significant — but these are design decisions with documented rationale, not hidden limitations.

For teams that need open-weight models they can deploy, fine-tune, and control, Gemma 4 is worth serious evaluation. If you’re also thinking about how AI tools compare for broader content workflows, our breakdown of Claude Pro vs ChatGPT Plus for affiliate marketing covers a different but adjacent comparison in detail.

Official Sources:

• Gemma 4 — Google DeepMind
• Gemma 4 Model Card — Google AI for Developers
• Gemma 4 Model Overview — Google AI for Developers
• Gemma Releases — Google AI for Developers