If you want to run Gemma 4 locally, you can choose from four practical paths depending on your goal and hardware.
1) Fastest start: Ollama (recommended)
This is the lowest-friction option for quick testing, daily chat, and local API usage.
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Highlights:
- Works on Windows, macOS, and Linux
- Handles hardware acceleration automatically
- Offers OpenAI-style local API compatibility
2) GUI workflow: LM Studio / Unsloth Studio
If you prefer a desktop UI instead of terminal commands:
- LM Studio: browse and run Gemma 4 quantized variants from Hugging Face (for example 4-bit, 8-bit), with resource visibility.
- Unsloth Studio: supports both inference and low-VRAM fine-tuning, often friendlier on 6GB-8GB GPUs.
3) Low-spec and maximum control: llama.cpp
Good for older hardware, CPU-focused setups, or users who want deeper runtime control.
With .gguf model files and quantization, Gemma 4 can be made practical on much smaller hardware budgets.
4) Developer integration: Transformers / vLLM
If you need Gemma 4 inside your own application:
- Transformers: straightforward Python integration
- vLLM: high-throughput inference for stronger GPU environments
Quick selection
| Need | Recommended tools | Hardware bar |
|---|---|---|
| I just want it running now | Ollama | Low |
| I want a ChatGPT-like UI | LM Studio | Medium |
| My VRAM is limited (6GB-8GB) | Unsloth / llama.cpp | Low |
| I am building local AI apps | Ollama / Transformers / vLLM | Medium to high |
| I need fine-tuning | Unsloth Studio | Medium to high |
Model size suggestion
Gemma 4 comes in multiple sizes (for example E2B, E4B, 31B).
- Start with quantized E2B/E4B on mainstream laptops
- Move to larger variants only after your baseline pipeline is stable