Runs in-browser via WebGPU — no installation required. Best for Electron apps and Raspberry Pi deployments. HuggingFace's most downloaded edge model with an Apache 2.0 license and full community model ecosystem.
This model requires a specialized High-VRAM environment. Ensure you have the latest CUDA Drivers or Metal Framework installed.
Minimum VRAM: 4GB VRAM Recommended
The SmolLM2-1.7B model by HuggingFace is a 1.7B dense parameter architecture optimized for chat tasks. It requires approximately 1.1GB of VRAM to comfortably run locally using a Q4_K_M 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 SmolLM2-1.7B 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 4GB 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 smollm2:1.7b' directly in your command line. Alternatively, you can search for the model via LM Studio's interface.