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Gemma 4 12B

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

Visit official site Read docs
Apache-2.0 License
Open sourceLocal firstSelf-hosted
Gemma 4 12B Apache-2.0 License deepmind.google verified 2026-06-04
About

Gemma 4 12B overview

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.

✦

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.

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.

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.

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.

Latency is one of the biggest practical barriers for local agents, especially when workflows require multiple reasoning turns.
Use cases

When to use Gemma 4 12B

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.

Compare

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.

FAQ

Questions

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.

Tags

Capabilities

local inferencetool callingopen sourceself hostedlocal firstopen weightslocal aiself hosted ai
Decision brief

Should you use Gemma 4 12B?

JSON
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
Trust and freshness
  • Verified 2026-06-04
  • License: Apache-2.0
  • No GitHub repo recorded
  • Open-source signal
Deployment

local, self hosted, cloud

Permission surface

shell/files, memory

Decision signals

Local first, Self-hostable

Agent packet

Structured decision data for Gemma 4 12B

This packet is the compact machine-readable view agents should use before following source links or taking action.

Full JSON Agent packet Markdown brief
Capabilities

local inference, tool calling

Constraints

open source, self hosted, local first, open weights

Deployment

local, self hosted, cloud

Permission surface

shell/files, memory

Recommended workflows

Coding agent workflow, Evaluation and observability, Local or private AI stack

Overview

What Gemma 4 12B does

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 to evaluate it

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.

Facts

Known metadata and operating surface

These fields are separated from editorial interpretation so agents can reason over facts and missing checks.

Resource type model
Category Models
Maturity active
Difficulty Unknown
License Apache-2.0
Pricing open source
Verified 2026-06-04
Source confidence medium
Risk level elevated
Fit matrix

Where Gemma 4 12B fits in an agent stack

strong

Coding agent workflow

Gemma 4 12B has multiple signals for coding agent workflow, including matching tags, capabilities, category, or positioning.

  • Run a small repository change and inspect the diff, tests, and rollback path.
  • Confirm official docs, current maintenance, license, and runtime constraints before production use.
strong

Evaluation and observability

Gemma 4 12B has multiple signals for evaluation and observability, including matching tags, capabilities, category, or positioning.

  • Add one repeatable test case and confirm results can run again in review or CI.
  • Confirm official docs, current maintenance, license, and runtime constraints before production use.
strong

Local or private AI stack

Gemma 4 12B has multiple signals for local or private ai stack, including matching tags, capabilities, category, or positioning.

  • Verify hardware requirements, data path, storage, and whether all calls stay in your environment.
  • Confirm official docs, current maintenance, license, and runtime constraints before production use.
partial

Browser automation

Gemma 4 12B has at least one signal for browser automation, but should be checked against a real task before adoption.

  • Run one non-sensitive website task and inspect clicks, waits, retries, and changed URLs.
  • Confirm official docs, current maintenance, license, and runtime constraints before production use.
partial

Memory or RAG workflow

Gemma 4 12B has at least one signal for memory or rag workflow, but should be checked against a real task before adoption.

  • Create, update, retrieve, correct, and delete memory or retrieval objects with real data.
  • Confirm official docs, current maintenance, license, and runtime constraints before production use.
partial

Reusable skill workflow

Gemma 4 12B has at least one signal for reusable skill workflow, but should be checked against a real task before adoption.

  • Run one skill end to end and check whether it produces evidence or structured output.
  • Confirm official docs, current maintenance, license, and runtime constraints before production use.
Inputs and outputs

What an agent should inspect

Likely inputs

  • Documents, user facts, entities, context, or retrieval queries
  • Prompts, messages, documents, images, or model inputs
  • Official setup instructions and a small real workflow

Likely outputs

  • Retrieved context, memory updates, graph relations, or citations
  • Scores, traces, regression results, dashboards, or failure cases
  • A decision on whether this resource fits the target workflow
Evidence

Sources, claims, and missing checks

Claims are marked separately from source links so future crawlers and reviewers can update them without rewriting the page.

Homepage homepage

Official or project-controlled source for this resource profile.

 Docs docs

Documentation source for setup, API shape, and operational behavior.

 Demo huggingface

Official or project-controlled source for this resource profile.

 Source homepage

Official or project-controlled source for this resource profile.

 Source homepage

Official or project-controlled source for this resource profile.
verified

Gemma 4 12B is listed as open source.

License metadata: Apache-2.0
inferred

Gemma 4 12B supports these recorded deployment modes: local, self hosted, cloud.

OpenAgent decision signal metadata.
inferred

Gemma 4 12B is tagged with local inference, tool calling capabilities.

OpenAgent capability taxonomy.
Missing checks
  • GitHub repository has not been recorded.
  • Repository freshness has not been recorded.
Next action

How to start evaluating Gemma 4 12B

Open Homepage

Start from the official source before adopting third-party instructions.

Open source

Read setup docs

Use docs as the source of truth for installation and supported interfaces.

Open source

Open Demo

Start from the official source before adopting third-party instructions.

Open source

Run Gemma 4 12B with Ollama

Use this after installing Ollama and confirming the local tag is available for your platform.

ollama run gemma4:12b
Compare

Alternatives and nearby resources

Use related resources to compare category fit, license, deployment model, and first-workflow behavior.

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Models LiteRT-LM

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Models
FAQ

Common questions about Gemma 4 12B

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.