Google's strong 4B model with multimodal capability and 128K context. One of the best small models.
This model requires a specialized High-VRAM environment. Ensure you have the latest CUDA Drivers or Metal Framework installed.
Minimum VRAM: 5GB VRAM Recommended
The Gemma 3 4B model by Google is a 4B parameter architecture optimized for chat tasks. It requires approximately 3GB of VRAM to comfortably run locally using a Q4_K_M quantization. Extending the context window up to 128,000 tokens will dynamically allocate further VRAM, meaning high-bandwidth memory hardware is strictly advised.
For running Gemma 3 4B at maximum tokens-per-second, we recommend using LM Studio or Ollama with a GGUF quantization (Q4_K_M or Q6_K). If you are multi-GPU, use vLLM to distribute the layers across your VRAM pool for optimal throughput.
You will need a GPU with at least 5GB of VRAM to run the Q4_K_M quantized version smoothly with a moderate context window.
The simplest method is utilizing Ollama by executing 'ollama run gemma3:4b' directly in your command line. Alternatively, you can search for the model via LM Studio's interface.