unsloth

Unsloth trains open models roughly twice as fast on about 70% less VRAM, which is the difference between renting a datacenter GPU and fine-tuning on the card already in your desktop. Fine-tuning means taking an existing open model and teaching it your specific task or data. Unsloth is now two things: Unsloth Core, the Python library and CLI, and Unsloth Studio, a local web UI you launch with a single command for downloading, running, and comparing models.

It runs on NVIDIA RTX 30/40/50 and Blackwell, on AMD across Windows, WSL, and Linux, and on macOS for both training and inference. CPU handles chat and data prep. Multi-GPU is supported. The reinforcement learning path claims 80% less VRAM for GRPO, and long-context training reaches 500K tokens. Studio also handles inference: GGUF, LoRA adapters, safetensors, tool calling, and API endpoints.

Free, with no paid tier and no commercial restriction on Core. Solo: this is the default way to fine-tune on consumer hardware. Teams: same answer, and multi-GPU scales it up.

The catch is the license split. Core is Apache 2.0 and carries no obligations. Studio's UI components are AGPL-3.0, and AGPL follows you if you embed that UI into a product you ship or host for others. For personal training runs it is irrelevant. For a company building a product on top of it, know which half you are standing on before you get far.