# Gemma 4 12B

Google DeepMind's 12B open multimodal model for local agentic workflows on laptops.

## Agent Decision Summary
- Risk level: elevated
- Source confidence: medium
- Recommended workflows: Coding agent workflow, Evaluation and observability, Local or private AI stack
- Permission surface: shell/files, memory
- Agent JSON: https://www.openagent.bot/models/gemma-4-12b.agent.json

## Summary
Gemma 4 12B is a mid-sized Apache 2.0 open model from Google DeepMind, designed to bring multimodal and agentic intelligence to consumer laptops with a reduced memory footprint.


## Guide
Gemma 4 12B is Google DeepMind's new mid-sized open model for local multimodal agents. It is designed to run on laptop-class hardware while supporting text, vision, and native audio inputs.

### What it is
Gemma 4 12B is an Apache 2.0 open model in the Gemma 4 family. It sits between the smaller E4B model and the larger 26B Mixture-of-Experts model, giving developers a more capable local target without requiring the largest memory footprint.

### Why it matters
Open model adoption increasingly depends on whether a model can run close to the user while still handling real multimodal and agentic tasks. Gemma 4 12B is important because Google is explicitly positioning it for laptop-local agents, native audio, streamlined vision, and reduced latency.

### How it works
Evaluate Gemma 4 12B by running your own prompt and multimodal test set. Compare quality, latency, memory use, tool behavior, audio and vision handling, license fit, and deployment path against nearby open models before adopting it.


## Use Cases
- Laptop-local AI agents: Gemma 4 12B is a candidate when you want an agent that can run on consumer hardware with local privacy and lower network dependency.
- Native audio and vision workflows: Test it for voice inputs, screenshots, images, documents, and multimodal assistant behavior.
- Mid-sized open model routing: Use it as a route between smaller edge models and larger workstation or server-grade models.

## Alternatives
- Use Gemma 4 E4B when edge deployment is the priority vs Gemma 4 E4B: E4B is better when memory and edge constraints dominate. Gemma 4 12B is better when you can spend more memory for stronger multimodal reasoning.
- Use Gemma 4 26B MoE when maximum Gemma 4 quality matters more than memory vs Gemma 4 26B MoE: The 26B MoE model is the larger target, but 12B is the practical laptop-class model to test first.

### Getting Started
- Read the launch post: https://blog.google/innovation-and-ai/technology/developers-tools/introducing-gemma-4-12b/
- Open Hugging Face: https://huggingface.co/collections/google/gemma-4
- Try Ollama: https://ollama.com/library/gemma4

### FAQ
- What is Gemma 4 12B?
  - Gemma 4 12B is Google's mid-sized Apache 2.0 open multimodal model for local agentic workflows on laptops.
- Can Gemma 4 12B run locally?
  - Google says Gemma 4 12B is small enough to run locally with 16GB of VRAM or unified memory. Teams should still test their own hardware, quantization, runtime, and latency requirements.
- What makes Gemma 4 12B different from older multimodal models?
  - Google describes Gemma 4 12B as encoder-free: vision and audio inputs are integrated directly into the LLM backbone instead of relying on separate multimodal encoders.
- Is Gemma 4 12B open source?
  - Gemma 4 12B is listed by Google under Apache 2.0. Re-check the official model card, license, and acceptable-use terms before production deployment.
- Should I use Gemma 4 12B for agents?
  - It is worth testing for local agents that need multimodal input, reasoning, and lower-latency laptop deployment, but you should benchmark tool behavior and failure modes on your own tasks.
## What It Does
Gemma 4 12B is an Apache 2.0 open model in the Gemma 4 family. It sits between the smaller E4B model and the larger 26B Mixture-of-Experts model, giving developers a more capable local target without requiring the largest memory footprint.

## How To Evaluate
Evaluate Gemma 4 12B by running your own prompt and multimodal test set. Compare quality, latency, memory use, tool behavior, audio and vision handling, license fit, and deployment path against nearby open models before adopting it.

## Why It Matters
Gemma 4 12B matters because it fills the gap between Google's edge-friendly E4B model and the larger 26B MoE model. It gives builders a more practical local model target for agents that need text, vision, audio, reasoning, and structured workflows without immediately moving to a large hosted model.


## Best For
- Developers testing local multimodal agents on laptops
- Teams that want a mid-sized open model before scaling to larger MoE models
- Builders evaluating audio, vision, and text workflows without separate multimodal encoders
- Product teams comparing open-weight models for private or self-hosted AI features

## Not For
- Teams that need Google's fully managed Gemini product experience
- Workloads that require the highest-quality frontier hosted model regardless of local deployment
- Deployments that cannot validate model cards, license terms, safety behavior, and serving costs before use

## What It Actually Does
- Mid-sized local agent target: Google positions Gemma 4 12B between the edge-friendly E4B model and the more advanced 26B Mixture-of-Experts model.
  - Why it matters: That makes it a useful evaluation point for teams that want stronger local reasoning without jumping straight to the largest model.
- Unified multimodal architecture: Gemma 4 12B uses an encoder-free architecture where vision and audio inputs flow directly into the LLM backbone.
  - Why it matters: Fewer separate multimodal components can reduce latency and memory overhead, which matters for laptop and local-agent use.
- Laptop-ready memory target: The launch describes Gemma 4 12B as small enough to run locally with 16GB of VRAM or unified memory.
  - Why it matters: A model that can run on consumer hardware is much easier to test for private assistants, offline prototypes, and controlled deployments.
- MTP drafters for lower latency: Gemma 4 12B ships with Multi-Token Prediction drafters intended to reduce latency.
  - Why it matters: Latency is one of the biggest practical barriers for local agents, especially when workflows require multiple reasoning turns.

## Typical Use Cases
- Local multimodal assistants: Use Gemma 4 12B to test assistants that combine text, images, and audio on laptop-class hardware.
- Agentic laptop workflows: Evaluate it for agents that need multi-step reasoning, local privacy, and structured task execution without relying entirely on hosted APIs.
- Audio and vision experiments: The native audio and streamlined vision path make it worth testing for meeting notes, voice inputs, screenshots, and document-style workflows.
- Open model routing: Compare Gemma 4 12B as a mid-sized local route between smaller edge models and larger 26B-class models.

## How It Compares
- Choose Gemma 4 12B for laptop-class multimodal agents vs Gemma 4 E4B: E4B is more edge-oriented, while 12B is the better candidate when you can afford more memory and want stronger reasoning and multimodal behavior.
- Choose Gemma 4 12B before the 26B MoE when memory matters vs Gemma 4 26B MoE: Google positions 12B as approaching 26B benchmark performance with less than half the memory footprint, so it is a practical first test for laptop agents.
- Benchmark it against Qwen, DeepSeek, Kimi, and Mistral vs other open model families: Gemma 4 12B has a strong local and multimodal story, but teams should still compare output quality, latency, tool behavior, license fit, and serving stack on their own workloads.

## Fit Matrix
- Coding agent workflow: strong. Gemma 4 12B has multiple signals for coding agent workflow, including matching tags, capabilities, category, or positioning. Required check: Run a small repository change and inspect the diff, tests, and rollback path.
- Evaluation and observability: strong. Gemma 4 12B has multiple signals for evaluation and observability, including matching tags, capabilities, category, or positioning. Required check: Add one repeatable test case and confirm results can run again in review or CI.
- Local or private AI stack: strong. Gemma 4 12B has multiple signals for local or private ai stack, including matching tags, capabilities, category, or positioning. Required check: Verify hardware requirements, data path, storage, and whether all calls stay in your environment.
- Browser automation: partial. Gemma 4 12B has at least one signal for browser automation, but should be checked against a real task before adoption. Required check: Run one non-sensitive website task and inspect clicks, waits, retries, and changed URLs.
- Memory or RAG workflow: partial. Gemma 4 12B has at least one signal for memory or rag workflow, but should be checked against a real task before adoption. Required check: Create, update, retrieve, correct, and delete memory or retrieval objects with real data.
- Reusable skill workflow: partial. Gemma 4 12B has at least one signal for reusable skill workflow, but should be checked against a real task before adoption. Required check: Run one skill end to end and check whether it produces evidence or structured output.

## Evidence
- verified: Gemma 4 12B is listed as open source. Source: License metadata: Apache-2.0
- inferred: Gemma 4 12B supports these recorded deployment modes: local, self hosted, cloud. Source: OpenAgent decision signal metadata.
- inferred: Gemma 4 12B is tagged with local inference, tool calling capabilities. Source: OpenAgent capability taxonomy.

## Missing Checks
- GitHub repository has not been recorded.
- Repository freshness has not been recorded.

## Next Actions
- Open Homepage: https://deepmind.google/models/gemma/gemma-4/
- Read setup docs: https://blog.google/innovation-and-ai/technology/developers-tools/introducing-gemma-4-12b/
- Open Demo: https://huggingface.co/collections/google/gemma-4
- Run Gemma 4 12B with Ollama: ollama run gemma4:12b

## Command Line
### Run Gemma 4 12B with Ollama
Use this after installing Ollama and confirming the local tag is available for your platform.

```bash
ollama run gemma4:12b
```

## Facts
- Category: models
- Resource type: model
- Open source: yes
- License: Apache-2.0
- Last verified: 2026-06-04

## Capabilities
- local-inference
- tool-calling

## Structured Use Case Tags
- local-ai
- self-hosted-ai

## Getting Started
- Read the official launch post: https://blog.google/innovation-and-ai/technology/developers-tools/introducing-gemma-4-12b/
- Open the Gemma 4 family page: https://deepmind.google/models/gemma/gemma-4/
- Download from Hugging Face: https://huggingface.co/collections/google/gemma-4
- Run with Ollama: https://ollama.com/library/gemma4
- Try Google AI Studio: https://aistudio.google.com/

## Links
- Homepage: https://deepmind.google/models/gemma/gemma-4/
- Docs: https://blog.google/innovation-and-ai/technology/developers-tools/introducing-gemma-4-12b/
- Demo: https://huggingface.co/collections/google/gemma-4
- Source: https://ollama.com/library/gemma4
- Source: https://aistudio.google.com/

## Structured Outputs
- JSON: https://www.openagent.bot/models/gemma-4-12b.json
- Markdown: https://www.openagent.bot/models/gemma-4-12b.md
- Agent JSON: https://www.openagent.bot/models/gemma-4-12b.agent.json
- Canonical: https://www.openagent.bot/models/gemma-4-12b