High-quality embedding model with 8K context. Outperforms OpenAI text-embedding-ada-002 on MTEB benchmark.
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 Nomic Embed Text model by Nomic AI is a 137M parameter architecture optimized for embedding tasks. It requires approximately 0.3GB of VRAM to comfortably run locally using a FP32 quantization. Extending the context window up to 8,192 tokens will dynamically allocate further VRAM, meaning high-bandwidth memory hardware is strictly advised.
For running Nomic Embed Text 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 nomic-embed-text' directly in your command line. Alternatively, you can search for the model via LM Studio's interface.