LLaMA Factory vs Unsloth

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

	LLaMA Factory Fine-tuning	Unsloth Fine-tuning
Tagline	Open-source, no-code WebUI for fine-tuning 100+ open LLMs with LoRA, QLoRA, DPO, and PPO.	Open-source LLM fine-tuning toolkit with custom kernels that train 2-30x faster and use up to 90% less VRAM.
Category	Fine-tuning	Fine-tuning
Pricing	Free· Free, open-source (Apache-2.0); self-hosted	Freemium· Free open-source; Pro and Enterprise contact sales
Model	Multi-model (LLaMA, Mistral, Qwen, Gemma, Phi, LLaVA, ChatGLM, Yi)	Llama, Mistral, Gemma, Qwen, GLM (multi-model)
Editorial score	7.2 / 10	8.2 / 10
Use cases	lora-fine-tuningqloradpo-alignmentinstruction-tuningrlhfvlm-fine-tuning	lora-finetuningqloralocal-trainingdpo-orpomodel-quantizationgguf-export
Pros	No-code WebUI (LlamaBoard) covers SFT, DPO, PPO, KTO, and reward modeling Supports 100+ open models including multimodal VLMs out of the box Full QLoRA stack (2-8 bit) plus LoRA+, DoRA, PiSSA variants Acceleration via FlashAttention-2, Unsloth, Liger Kernel, vLLM inference Exports to GGUF / Ollama and integrates with W&B, MLflow, TensorBoard	Real, measurable 2-5x speedups and big VRAM savings on consumer GPUs Open-source core with permissive license and active GitHub Drop-in compatible with Hugging Face TRL, PEFT and transformers Excellent ready-to-run Colab notebooks for most popular models Exports cleanly to GGUF/llama.cpp, vLLM and Ollama
Cons	Self-hosted only — you bring the GPUs and the ops Rapid release cadence means version pinning is essential WebUI abstracts but does not solve VRAM and dataset-formatting pitfalls	Multi-GPU and multi-node are gated behind paid tiers with opaque pricing Not a hosted service — you still bring your own GPU and MLOps Cutting-edge model support sometimes lags official releases by days
Website	llamafactory.readthedocs.io	unsloth.ai

Pick LLaMA Factory if

✅ No-code WebUI (LlamaBoard) covers SFT, DPO, PPO, KTO, and reward modeling
✅ Supports 100+ open models including multimodal VLMs out of the box
✅ Full QLoRA stack (2-8 bit) plus LoRA+, DoRA, PiSSA variants
✅ Acceleration via FlashAttention-2, Unsloth, Liger Kernel, vLLM inference

Pick Unsloth if

✅ Real, measurable 2-5x speedups and big VRAM savings on consumer GPUs
✅ Open-source core with permissive license and active GitHub
✅ Drop-in compatible with Hugging Face TRL, PEFT and transformers
✅ Excellent ready-to-run Colab notebooks for most popular models

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