Unsloth

Unsloth is an open-source (Apache 2.0) framework that accelerates LLM fine-tuning and training while drastically cutting memory usage, making it feasible to fine-tune popular open models on consumer or free-tier GPUs. It cites speedups up to around 30x and roughly 90% less memory in some configurations, and has huge community adoption. The free library is fully functional; paid Pro and Enterprise tiers add faster kernels, longer context, and multi-GPU/multi-node training.

Unsloth solves a practical pain point in open-model fine-tuning: training and fine-tuning LLMs is often slow and memory-hungry, putting it out of reach on modest hardware. Unsloth uses hand-optimized kernels and memory tricks to accelerate fine-tuning (the project cites speedups up to around 30x in some configurations and roughly 90% less memory usage), making it possible to fine-tune popular open models on a single consumer or free-tier GPU. The open-source core is released under Apache 2.0 and has seen enormous community adoption, with tens of thousands of GitHub stars and millions of monthly model downloads. It supports popular open models and integrates naturally into common notebook and training workflows, which has made it a go-to for researchers, hobbyists, and startups fine-tuning models on a budget. Unsloth (the company) went through Y Combinator (Summer 2024) and has backing that has been reported to include Y Combinator, GitHub Accelerator, and Microsoft's M12, among others, with relatively modest disclosed early funding. It monetizes through paid tiers: a low-cost Pro option adding faster kernels, longer context, and priority support, and Pro/Enterprise tiers that unlock multi-GPU and multi-node training via sales. For most individual users, the free open-source library is fully functional.