Math Index 91.0 — the highest math score at the 8GB VRAM tier. NVIDIA's distilled Llama-3.1 with proprietary reward model training. Ideal for STEM tutoring and quantitative analysis on a single mid-range GPU.
This model requires a specialized High-VRAM environment. Ensure you have the latest CUDA Drivers or Metal Framework installed.
Minimum VRAM: 7GB VRAM Recommended
The NVIDIA Nemotron Nano 8B model by NVIDIA is a 8B dense parameter architecture optimized for reasoning tasks. It requires approximately 5GB 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 NVIDIA Nemotron Nano 8B 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 7GB 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 nemotron-nano:8b' directly in your command line. Alternatively, you can search for the model via LM Studio's interface.