Outstanding performance at the 24GB VRAM tier with a native Google vision-language encoder integrated directly into the weights. MMLU-Pro score of 67.5.
This model requires a specialized High-VRAM environment. Ensure you have the latest CUDA Drivers or Metal Framework installed.
Minimum VRAM: 19GB VRAM Recommended
The Gemma 3 27B model by Google is a 27B dense parameter architecture optimized for chat tasks. It requires approximately 17GB of VRAM to comfortably run locally using a Q4_K_M quantization. Extending the context window up to 131,072 tokens will dynamically allocate further VRAM, meaning high-bandwidth memory hardware is strictly advised.
For running Gemma 3 27B 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 19GB 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 gemma-3-27b' directly in your command line. Alternatively, you can search for the model via LM Studio's interface.