unsloth vs peft
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
peft
Python library for LoRA and other parameter-efficient fine-tuning of Transformers models
Verdict
unsloth ranks higher, 88 to 78. Of the 4 score parts we measure for both, unsloth leads on 2, peft on 1 and 1 is even.
- Adoptionunsloth leads by 47 points, 96 to 49: a wide gap.
- FreshnessEven at 100.
- Maintenanceunsloth leads by 9 points, 97 to 88.
- Agent-readypeft leads by 70 points, 70 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 peft if you are
ML engineers fine-tuning LLMs and diffusion models on limited GPU memory
- Adapter checkpoints are MBs instead of GBs; 19MB vs 11GB for T0_3B
- LoRA trained a 12B model (mt0-xxl) on one 80GB GPU where full finetuning ran out of memory
| What we compare | unsloth | peft |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 96, widely used | 49, known |
| Freshness | 100, active | 100, active |
| Maintenance | 97, healthy | 88, healthy |
| Easy to run | Not scored for this kind | Not scored for this kind |
| Agent-ready | 0, none | 70, partly |
| Facts from GitHub and the README | ||
| Stars | 78.1k | 21.8k |
| License | Apache-2.0 (permissive) | Apache-2.0 (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Oct 2026 |
| Language | Python | Python |
| Docker | Yes | Yes |
| GPU | Optional | Optional |
| 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
peft
PEFT wraps a pretrained model with adapter methods such as LoRA, soft prompts and IA3 so only a small set of extra parameters is trained. Adapters are saved as small checkpoints (19MB versus 11GB for T0_3B in the README's example). It integrates with Transformers, Diffusers, Accelerate and TRL, and can be combined with quantization such as QLoRA.
Who it is for: ML engineers fine-tuning LLMs and diffusion models on limited GPU memory
Strengths
- Adapter checkpoints are MBs instead of GBs; 19MB vs 11GB for T0_3B
- LoRA trained a 12B model (mt0-xxl) on one 80GB GPU where full finetuning ran out of memory
- Integrates with Transformers, Diffusers, Accelerate and TRL; works with quantized base models
- Install with pip; get_peft_model wraps an existing model in a few lines
Weaknesses
- Library only: no training UI, scheduler or serving; training loop is your own or Trainer
- Transformers integration lacks some PEFT features, such as merging adapters into the base model
- Models without official support need manual config for target modules
- Memory figures in the README assume an A100 80GB with over 64GB CPU RAM
- GPU optional
- Docker
- Needs PyTorch, Transformers
- Models: LoRA, Soft prompts, IA3, QLoRA, Transformers models
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