The guide shows how to run the Flint model locally on 24GB RAM using Unsloth Dynamic GGUFs, with quantization sizes and benchmark context.
Run Flint locally on 24GB RAM via Unsloth Dynamic GGUFs!
Flint is the strongest MoE model under 50B parameters.
Recursive’s Apache-2.0 model has 39.8B parameters (2.5B active). Run at 125 t/s on Mac, 150 on RTX Spark
GGUF: https://huggingface.co/unsloth/flint-GGUF
Guide: https://unsloth.ai/docs/models/flint
Introducing Flint, @Recursive_SI’s first model: a fast local coding model built with our automated research system, the Eureka Machine. • Best-in-class fast local coding • Up to 2x faster than comparable models: 125+ tokens/sec on a MacBook Pro, 150+ on RTX Spark • Built from scratch in under 4 months Private, unmetered intelligence on your own machine. Open weights available now on @huggingface, run it locally on your favorite framework or try it for free on @opencode or @pidotdev.
Unsloth AI · x.comPublished · added here
Original title: Run Flint locally on 24GB RAM via Unsloth Dynamic GGUFs!
