Non-Transformer architecture with linear context scaling — never degrades on long sequences. Achieves 40,400 tokens/sec on Apple Silicon. The fastest local model for structured extraction pipelines and IoT edge nodes.
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 LFM2-350M model by Liquid AI is a 350M parameter architecture optimized for chat tasks. It requires approximately 0.3GB of VRAM to comfortably run locally using a FP16 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 LFM2-350M 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 FP16 quantized version smoothly with a moderate context window.
The simplest method is utilizing Ollama by executing 'ollama run lfm2:350m' directly in your command line. Alternatively, you can search for the model via LM Studio's interface.