unsloth vs llamafactory
Two of the top projects in Training and fine-tuning, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
unsloth
Desktop and web app to run, fine-tune and export open models
llamafactory
Fine-tuning toolkit for 100+ LLMs with a CLI and Gradio web UI
Verdict
unsloth ranks higher, 87 to 72. Of the 4 score parts we measure for both, unsloth leads on 2, llamafactory on 1 and 1 is even.
- Adoptionunsloth leads by 9 points, 93 to 84.
- FreshnessEven at 100.
- Maintenanceunsloth leads by 59 points, 97 to 38: a wide gap.
- Agent-readyllamafactory leads by 55 points, 55 to 0: a wide gap.
Pick unsloth if you are
Engineers fine-tuning and locally running open LLMs on their own GPUs
- Fine-tuning covers LoRA, QLoRA, full fine-tuning, GRPO, DPO and FP8 pretraining
- Claims 2x faster training with up to 70% less VRAM, per README
Pick llamafactory if you are
Engineers fine-tuning open LLMs on their own GPUs, with or without code
- Broad method coverage: SFT, PPO, DPO, KTO, ORPO, LoRA, QLoRA via AQLM/AWQ/GPTQ/HQQ/EETQ
- Supports GaLore, BAdam, APOLLO, Muon, DoRA, PiSSA, plus Unsloth and Liger Kernel speedups
| What we compare | unsloth | llamafactory |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 93, widely used | 84, widely used |
| Freshness | 100, active | 100, active |
| Maintenance | 97, healthy | 38, patchy |
| Easy to run | Not scored for this kind | Not scored for this kind |
| Agent-ready | 0, none | 55, partly |
| Facts from GitHub and the README | ||
| Stars | 78k | 75.5k |
| License | Apache-2.0 (permissive) | Apache-2.0 (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | May 2026 |
| Language | Python | Python |
| Docker | Yes | Yes |
| GPU | Optional | Required |
| arm64 or Apple Silicon | Mentioned | Mentioned |
unsloth
Unsloth ships as a desktop app, a web UI (Studio) and a Python library for running and fine-tuning models. It runs GGUF and other open models locally with an OpenAI-compatible API, and trains with LoRA, QLoRA, full fine-tuning and RL methods such as GRPO and DPO. The README claims 2x faster training with up to 70% less VRAM, and exports to GGUF, FP8 and NVFP4.
Who it is for: Engineers fine-tuning and locally running open LLMs on their own GPUs
Strengths
- Fine-tuning covers LoRA, QLoRA, full fine-tuning, GRPO, DPO and FP8 pretraining
- Claims 2x faster training with up to 70% less VRAM, per README
- Exports to GGUF, FP8 and NVFP4 and serves an OpenAI-compatible API
- Runs on NVIDIA, AMD, Intel, CPU and Vulkan across Windows, macOS and Linux
Weaknesses
- Scope is broad: desktop app, Studio web UI and Core library each install differently
- Server-side tools are on by default; exposing it needs --disable-tools or a strong password
- Install relies on curl or irm piped to shell scripts
- Speed and VRAM figures are the project's own claims; hardware requirements are not listed
- GPU optional
- Docker
- Needs PyTorch, llama.cpp, audio.cpp, Cloudflare (optional remote access)
- Models: GGUF, MLX, LoRA, QLoRA, diffusion
- port 8000
llamafactory
LlamaFactory fine-tunes language and multimodal models through YAML-configured CLI runs or the LLaMA Board Gradio UI. It covers pre-training, SFT, reward modeling, PPO, DPO, KTO and ORPO, with full-tuning, freeze-tuning, LoRA and 2 to 8-bit QLoRA. Trained models can be served through an OpenAI-style API using vLLM or SGLang workers.
Who it is for: Engineers fine-tuning open LLMs on their own GPUs, with or without code
Strengths
- Broad method coverage: SFT, PPO, DPO, KTO, ORPO, LoRA, QLoRA via AQLM/AWQ/GPTQ/HQQ/EETQ
- Supports GaLore, BAdam, APOLLO, Muon, DoRA, PiSSA, plus Unsloth and Liger Kernel speedups
- Covers multimodal and audio models such as Qwen3-VL, LLaVA and Qwen2-Audio
- Runs on NVIDIA, AMD ROCm and Ascend NPU, with hosted Colab and PAI-DSW notebooks
Weaknesses
- Official documentation is marked WIP in the README
- No VRAM or RAM figures in the visible README; hardware requirements unknown here
- Web UI is for training and chat, not a production serving layer
- Many optional backends (vLLM, SGLang, Unsloth, KTransformers) add install complexity
- GPU required
- Docker + Compose
- Needs PyTorch, Hugging Face Transformers, Gradio, vLLM (optional), SGLang (optional)
- Models: LLaMA, LLaVA, Mistral, Mixtral-MoE, Qwen3
More in Training and fine-tuning
Building with these?
We help teams pick an open-source AI stack and run it in production. Client work never changes a rank.