State-of-the-art embedding model for retrieval tasks. Ranks #1 on multiple MTEB categories.
This model requires a specialized High-VRAM environment. Ensure you have the latest CUDA Drivers or Metal Framework installed.
Minimum VRAM: 3GB VRAM Recommended
The mxbai-embed-large model by MixedBread AI is a 335M parameter architecture optimized for embedding tasks. It requires approximately 0.7GB of VRAM to comfortably run locally using a FP32 quantization. Extending the context window up to 512 tokens will dynamically allocate further VRAM, meaning high-bandwidth memory hardware is strictly advised.
For running mxbai-embed-large 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 3GB of VRAM to run the FP32 quantized version smoothly with a moderate context window.
The simplest method is utilizing Ollama by executing 'ollama run mxbai-embed-large' directly in your command line. Alternatively, you can search for the model via LM Studio's interface.