Unsloth Unsloth
Unsloth is a high-performance framework and local environment for running and training LLMs, vision, and diffusion models. It provides optimized Triton kernels that significantly reduce VRAM usage and increase training speed while maintaining model accuracy.
Added support for Qwen3.6 and MTP inference, improved AMD GPU compatibility across Windows/WSL/Linux, and introduced new long-context RL batching algorithms.
As of
- Optimized training kernels for 2x faster performance and 70% less VRAM
- Unsloth Studio web UI with data recipe workflows and visual observability
- Support for Reinforcement Learning (GRPO, DPO, FP8) and long-context training
- Agentic integrations including self-healing tool calling and code execution
- Export capabilities to GGUF, 16-bit safetensors, and OpenAI-compatible API endpoints
Developers and researchers who need to perform memory-efficient, accelerated local fine-tuning and inference of open-source models on single or multi-GPU setups.
Users requiring complex multi-node orchestration or advanced distributed parallelism configurations, which are better served by frameworks like Axolotl or TRL.
Unsloth uses a dual-licensing model where the core library is under Apache 2.0 and the Studio web UI is under AGPL-3.0.
Unsloth is the gold standard for efficient, high-speed local LLM training, offering a rare combination of cutting-edge research implementations and user-friendly no-code tools.
Is Unsloth free?
Yes - Unsloth is Open Source. Unsloth uses a dual-licensing model where the core library is under Apache 2.0 and the Studio web UI is under AGPL-3.0.
What is Unsloth best for?
Developers and researchers who need to perform memory-efficient, accelerated local fine-tuning and inference of open-source models on single or multi-GPU setups.
Who makes Unsloth?
Unsloth is developed by Unsloth AI. It is listed in the AI-Native IDEs & Editors category on ai.dosa.dev.
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