The classic vision-language model. Describe images, answer visual questions locally. Proven and reliable.
This model requires a specialized High-VRAM environment. Ensure you have the latest CUDA Drivers or Metal Framework installed.
Minimum VRAM: 8GB VRAM Recommended
The LLaVA 7B model by Haotian Liu et al. is a 7B parameter architecture optimized for vision tasks. It requires approximately 5.5GB of VRAM to comfortably run locally using a Q4_K_M quantization. Extending the context window up to 4,096 tokens will dynamically allocate further VRAM, meaning high-bandwidth memory hardware is strictly advised.
For running LLaVA 7B 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 8GB 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 llava' directly in your command line. Alternatively, you can search for the model via LM Studio's interface.