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The reliable route is to pick a Gemma 4 size whose memory estimate fits your machine with room to spare, install a maintained runtime, confirm a one-line prompt works, and only then add a chat window, a local API, or an application. Ollama is the most direct command-line route. LM Studio is the simpler choice if you prefer a desktop chat interface. Both are documented by Google as local options for Gemma 4.
Which Gemma 4 model your computer can run
Gemma 4 comes in five sizes: E2B, E4B, 12B, 26B A4B, and 31B. Larger models and higher-precision files need more memory and more compute. Google’s Gemma 4 model overview gives approximate inference memory for each size at three precisions. The figures are model-loading estimates, not performance guarantees, and they include a 20% loading overhead.
| Model | BF16 | SFP8 | Q4_0 |
|---|---|---|---|
| E2B | 11.4 GB | 5.7 GB | 2.9 GB |
| E4B | 17.9 GB | 8.9 GB | 4.5 GB |
| 12B | 26.7 GB | 13.4 GB | 6.7 GB |
| 26B A4B | 57.7 GB | 28.8 GB | 14.4 GB |
| 31B | 69.9 GB | 34.9 GB | 17.5 GB |
Source: Google AI for Developers, Gemma 4 model overview (page accessed 2026-10-07). Values are approximate and vary by inference tool and environment.
Read the table as a floor, not a target. A model that loads with 0.5 GB to spare will often run slowly once your context grows, your browser takes memory, or you run other applications. Leave headroom above the estimate, especially for longer conversations.
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For the 12B model specifically, Google’s developer guide by André Susano Pinto, Research Engineer, dated June 3, 2026, states: “Gemma 4 12B is small enough to run locally on dedicated GPU laptops with 16GB VRAM or unified memory.” That statement applies to the 12B model only. It does not establish the same experience for the other sizes or for heavier workloads.
Hardware checklist before you download
- Memory type and capacity: dedicated GPU VRAM or unified memory determines what you can load. Check which applies to your machine.
- Operating system and runtime support: confirm that your chosen runtime supports your OS and accelerator before you download a large file.
- Free disk space: the model file itself takes space on top of the loading estimate.
- Intended use: a short chat needs less headroom than long documents or several simultaneous requests.
What quantization changes
Quantized GGUF files store model values at lower precision, which reduces memory and compute. Google’s Ollama integration guide states the trade-off directly: “Using less precise data in quantized models to process requests typically lowers the quality of the models output, but with the benefit of also lowering the compute resource costs.”
In practice, moving from BF16 to Q4_0 cuts the loading estimate substantially, as the table shows. The quality cost depends on the task. Summarizing a document, writing code, and answering factual questions can respond differently to the same compression. Google does not publish a quality ranking across quantization methods in the sources used here, so test representative prompts yourself after changing the model, quantization, runtime, or context length.
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Option A: Set up Gemma 4 with Ollama
Google’s official Ollama integration guide documents this sequence. Follow it in order, and stop at any step that fails before continuing.
- Install Ollama. Download the installer for your operating system from the Ollama download page and follow its instructions.
- Confirm the command is available. Open a terminal and run
ollama --version. If the shell reports that the command is not found, the Ollama executable is not on your operating system’s path. Reopen the terminal after installation, and check the path settings if the error persists. - Download a Gemma 4 model. Run
ollama pull gemma4to fetch the default Gemma 4 entry. If you need a specific size, name its tag instead. The guide listsgemma4:e2b,gemma4:e4b,gemma4:26b, andgemma4:31b. Google’s overview also includes the 12B model, but the guide does not list a 12B tag, so check the Ollama model library for the current tag name before pulling it. - Check what is installed. Run
ollama list. The model you pulled should appear with its size. - Send a first prompt. Run
ollama run gemma4 "roses are red"for a single response, or runollama run gemma4to open an interactive session. Wait for a short, coherent reply before you configure anything else.
A successful run means the model loaded and generated text on your hardware. It does not show how fast or how well the model will handle your real work. Test that separately with prompts you actually care about.
Option B: Use LM Studio for a desktop chat interface
Google’s run guide lists LM Studio alongside Ollama as a local chat interface. Its overview maps GGUF QAT checkpoints to llama.cpp and LM Studio for local CPU, Apple Silicon, or consumer-GPU use. Install LM Studio, download a Gemma 4 GGUF build that matches a size from the memory table, and load it in the chat view. Start with a short conversation and confirm the reply before you rely on it. Choose LM Studio if you want a graphical interface and do not need the command-line workflow.
Other runtimes and when to consider them
Google groups its local tools by use case rather than ranking them, so the choice depends on your hardware and what you need to control.
| Runtime | Google’s documented use | Best fit | Notes from the sources |
|---|---|---|---|
| Ollama | Local chat UI and local API | Command-line users who want a simple pull-and-run workflow | Local API at http://localhost:11434 |
| LM Studio | Local chat UI | Users who prefer a desktop interface | Uses GGUF QAT checkpoints |
| llama.cpp | Efficient edge use; CPU, Apple Silicon, consumer GPU | More direct command-line configuration | Uses GGUF QAT checkpoints |
| MLX | Efficient edge use | Apple Silicon | Apple-focused framework |
| LiteRT-LM | Local desktop and on-device use | Mobile-optimized formats for E2B and E4B | Google’s developer guide demonstrates importing a 12B LiteRT-LM checkpoint and serving it |
| Transformers, Keras, Tunix, Unsloth | Development and fine-tuning | Custom Python applications and training | Not a chat-first route |
For LiteRT-LM, Google’s developer guide shows launching litert-lm serve after importing the 12B checkpoint, which provides an OpenAI-compatible local API server. Use that route if your application already speaks the OpenAI API format. Google’s sources do not establish a current speed ranking across these runtimes, so do not choose on that basis alone.
Using the local API safely
Ollama exposes a local API. For development, the documented generate endpoint is http://localhost:11434/api/generate. A test call from the same machine confirms that your application can reach the model.
Keep this endpoint on your own machine. localhost means only processes on that computer can reach it. Do not expose it to your local network or the internet without deliberate access controls, such as a reverse proxy with authentication, because anyone who reaches it can run prompts against your hardware.
Troubleshooting
- The
ollamacommand is not found. Confirm the installation finished, reopen the terminal, and verify that the Ollama executable directory is on your operating system’s path. - No model appears in
ollama list. The pull did not complete. Runollama pull gemma4again, then checkollama list. - The model will not load or fails with an out-of-memory error. Move down one size or one precision level, using the memory table as the guide, and close other memory-heavy applications.
- The model loads but responds slowly. This is common when the model is close to your memory limit. Step down in size or quantization, shorten the context, and retest. Performance depends on hardware and runtime, so compare results only on the same machine and configuration.
- Replies are weak for your task. Test a larger size or a less aggressive quantization if your memory allows it. Reduced precision typically lowers output quality, so the trade-off is expected rather than a fault.
Adding images and applications
Add image input, a graphical front end, or an application only after the text-only Ollama or LM Studio setup works. Each addition introduces a new variable, so keeping the base setup verified makes problems easier to isolate. Confirm that your chosen runtime and model build support the feature you need before you rely on it, because the sources reviewed here do not establish image support for every runtime and format.
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