Workflows/Ask questions about an image privately with Gemma 4

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Ask questions about an image privately with Gemma 4

Use Gemma 4 12B through Ollama to answer questions about a local image without uploading it to a hosted model. Check its answer against the visible photo.

EstimatedSource checked 9/29/2026

LOCALRENTED GPU

Freshness 52 out of 100. Repository age: 169 days old, May 23, 2026. Measures age, not quality.

Popularity 85 out of 100. 1,846,252 downloads in 30 days; 1,725 lifetime likes. Checked Nov 7, 2026. Measures adoption, not quality.

What do these scores mean?

Two ways to compare models. Neither score measures answer quality, benchmark performance, or whether a model fits your machine.

Freshness · release age

A new release starts at 100. The score halves every 180 days: 100 × 2^(−age in days / 180). When the release date is unavailable, a repository date is a fallback—not proof of when the model was released. Missing dates stay unscored.

Check the date source

Popularity · adoption

Downloads over the past 30 days carry 80% of the score; lifetime likes carry 20%. Both use a logarithmic scale, capped at 10 million downloads and 100,000 likes. If likes are unavailable, downloads carry all the weight. Missing download statistics stay unscored.

round(80 × min(1, log10(1 + downloads) / 7) + 20 × min(1, log10(1 + likes) / 5))

Check the statistics source

Scores as of Nov 8, 2026. Statistics checked Nov 7, 2026. Statistics are marked for refresh after 72 hours.Full scoring method

Before you begin

Hardware

Memory starting points for one 7.6 GB Gemma 4 12B image question at short context. The vision encoder and image processing use more memory than text-only chat; these are estimates.

Machine	Gemma 4 12B · vision
NVIDIA GPU · 8 GB	Use CPU offload with enough system RAM; expect slow image answers.
NVIDIA GPU · 12 GB	May run with CPU offload; image input leaves little GPU headroom.
NVIDIA GPU · 16 GB	Runs a short image question; verify GPU offload.
NVIDIA GPU · 24 GB	Runs with more headroom; verify the GPU backend.
NVIDIA GPU · 32 GB	Runs with more headroom; verify the GPU backend.
AMD Radeon · 8 GB	Use CPU offload with enough system RAM; expect slow image answers.
AMD Radeon · 16 GB	Runs a short image question; verify GPU offload.
AMD Radeon · 24 GB	Runs with more headroom; verify the GPU backend.
AMD Radeon · 32 GB	Runs with more headroom; verify the GPU backend.
Intel Arc · 8 GB	Use CPU offload with enough system RAM; expect slow image answers.
Intel Arc · 16 GB	Runs a short image question; verify GPU offload.
Intel Arc · 24 GB	Runs with more headroom; verify the GPU backend.
Intel Arc · 32 GB	Runs with more headroom; verify the GPU backend.
Apple Silicon · 16 GB unified	Possible with memory pressure; use a 24 GB-plus Mac for a reliable start.
Apple Silicon · 24 GB unified	Runs one short image question; limited headroom.
Apple Silicon · 32 GB unified	Runs one short image question with more headroom.
Apple Silicon · 64 GB unified	Runs one short image question with more headroom.
CPU-only · 16 GB RAM	May need more RAM for image input; choose a larger machine.
CPU-only · 32 GB RAM	Can run in RAM; expect slow image answers.
CPU-only · 64 GB RAM	Can run in RAM; expect slow image answers.

AMD and Intel need a working Ollama GPU backend; run ollama ps to confirm actual placement. Larger images and longer context raise memory use. Only a local image file is sent to Ollama.

IMAGE UNDERSTANDING / OLLAMASOURCE-LINKED SETUP

Ask Gemma 4 about an image that stays on your machine.

Install the 12B vision package, save a sample cat photo locally, ask one specific question through Ollama and compare the reply with the picture. Choose the instructions for your operating system.

Choose a package Run its commands Check the result

BEFORE YOU BEGIN

The documented package

This setup uses the package documented for this task. Review its source and supported platforms before starting.

Save your machine in My Hardware to get an automatic starting choice. You can always choose any package yourself.

CURRENT SETUP

Gemma 4 · 12B vision · official

Start with 4K text context and one ordinary JPEG on 16 GB GPU memory or 24 GB Apple Silicon unified memory. A 12 GB GPU may spill into system RAM; use ollama ps to check. Larger images and longer context increase memory use. This is an estimate, not a benchmark.

Freshness 52 out of 100. Repository age: 169 days old, May 23, 2026. Measures age, not quality.

Popularity 85 out of 100. 1,846,252 downloads in 30 days; 1,725 lifetime likes. Checked Nov 7, 2026. Measures adoption, not quality.

What do these scores mean?

Two ways to compare models. Neither score measures answer quality, benchmark performance, or whether a model fits your machine.

Freshness · release age

Check the date source

Popularity · adoption

round(80 × min(1, log10(1 + downloads) / 7) + 20 × min(1, log10(1 + likes) / 5))

Check the statistics source

Scores as of Nov 8, 2026. Statistics checked Nov 7, 2026. Statistics are marked for refresh after 72 hours.Full scoring method

View weight source

THE WORKFLOW

Set up Gemma 4 · 12B vision on your machine

Choose where you want to run it. The instructions below update to match your selection.

CHOOSE YOUR SETUPMac

Use Terminal on Apple Silicon. Ollama for macOS is available from the official download page.

Install Ollama for macOS

Download the Mac application from Ollama, move it to Applications, and open it so the local service starts.

Download Ollama for Mac

Check Ollama in Terminal

Open a new Terminal window and confirm the command-line tool is available.

ollama --version

Download the Gemma 4 vision package

Download the exact 12B tag from the official Ollama library. This is the same package used in the image command below.

ollama pull gemma4:12b

Save the example image locally

Open the linked example cat photo from Ollama’s vision guide and save it as cat.jpg in your current working folder. You can substitute another permitted JPEG after the sample succeeds.

Download the sample cat photo Read Ollama vision instructions

Ask about the saved image

In Terminal, run the vision command with local cat.jpg and save its text answer to image-answer.txt in this folder. Ask which animal appears and describe one visible detail. Open the JPEG and read the saved answer to check it; do not trust details the image does not show.

ollama run gemma4:12b ./cat.jpg "Which animal is in this image? Describe one visible detail." | tee image-answer.txt

Confirm the local package

In a second Terminal window, verify that the selected package is installed locally.

ollama list

SUCCESS CHECK · Gemma 4 · 12B vision

An answer grounded in your local image

Gemma 4 returns a description of the sample cat photo. Open the same JPEG and check that the main subject and visible setting match the reply; the model may still miss subtle details.

Only use images you are allowed to process. The example photo comes from the official Ollama vision documentation. An image increases memory use beyond a text-only 4K chat estimate.

Back to setup steps

When it doesn’t go to plan

Ollama reports it cannot open the image path.

Save the image as cat.jpg in the folder where your Terminal or PowerShell is open, then use the platform-specific relative path shown in the guide. Confirm the extension is .jpg, not a web page.

The model loads slowly or runs out of memory.

Use a smaller JPEG, close other GPU apps, keep the question short, then check ollama ps for CPU offload. Move to a larger GPU or 24 GB-plus Mac if this remains too slow.

The answer describes objects that are not in the photo.

Compare the reply with the visible image. Ask one focused question at a time and treat uncertain or small details as unverified.

REFERENCE LIBRARY

Sources and files

Original instructions, model files, and compatibility notes behind this setup.

4 SOURCES

01Ollama vision input and image-path commanddocs.ollama.com 02Ollama Gemma 4 12B text and image packageollama.com 03Install Ollamaollama.com 04Ollama GPU and driver supportdocs.ollama.com

The model behind this workflow

Gemma 4 · 12B

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Text

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LOCALRENTED GPU

Verified source