What it does
Core capabilities at a glance
- 2x faster training with manual autograd optimization kernels
- 50% memory reduction via optimized attention
- Supports Llama 3, Mistral, Gemma, DeepSeek, Qwen, and more
- 4-bit and 8-bit QLoRA support
- LoRA and GaLore fine-tuning methods
- Free Google Colab notebooks for training
Deep dive
The full breakdown - performance, comparisons, and setup
Unsloth
Unsloth is the tool that made LLM fine-tuning accessible to anyone with a consumer GPU. Before Unsloth, fine-tuning a 7B model required 24GB+ VRAM. With Unsloth, you can do it on 6GB.
What it is
Unsloth is a Python library that optimizes the training loop for HuggingFace Transformers. It replaces the attention and linear layer implementations with manually optimized CUDA kernels that use less memory and run faster.
Performance you'll see
| Model | VRAM (standard) | VRAM (Unsloth 4-bit) | Speedup |
|---|---|---|---|
| Llama 3 8B | 24GB | 6GB | 2.1x |
| Qwen3 30B | 48GB | 16GB | 1.8x |
| Mistral 7B | 16GB | 4GB | 2.3x |
| Llama 3.3 70B | 140GB | 48GB | 1.7x |
How it stacks up
| Unsloth | Axolotl | LLaMA-Factory | LitGPT | |
|---|---|---|---|---|
| Memory optimization | ✓✓✓ | ✓ | ✓ | ✓ |
| Ease of use | High | Medium | High (web UI) | Medium |
| Training methods | LoRA, QLoRA | Full, LoRA, QLoRA | Full, LoRA, QLoRA | Full, LoRA |
| Colab friendly | ✓ | Limited | Limited | ✗ |
| Best for | Quick fine-tuning | Config-heavy | Web UI training | Full training |
Get started
pip install unsloth
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
model_name="unsloth/Qwen3-8B-bnb-4bit",
max_seq_length=4096,
load_in_4bit=True,
)
model = FastLanguageModel.get_peft_model(model, r=16, lora_alpha=16)
# Train with your datasetWhen to use something else
- Fine-tuning via web UI: LLaMA-Factory
- Configuration-heavy training: Axolotl with YAML configs
- Full pretraining: LitGPT or Torchtune
From the community
What users are saying
“I am unaware lm studio is being used for fine tuning. I believe it only does inference. Happy to see unsloth making it even easier for people like me to get going with fine tuning. Not that I am unable to I'm just lazy. Fine tuning with a UI is definitely targeted towards hobbyists.”
“Hey Unsloth, your gguf are the first ones I look for when I want to download a gguf model. Today I was trying in fact to see, what's the smallest Qwen3.8-27B that I could run and get good results, say restricting it to 16GB of ram.. so I went, pick up the Qwen3.8-27B-UD-IQ2_XXS.gguf and them BAM, error on MTP...”
“ICYMI unsloth has had some major breakthroughs today with the Qwen3.5 local models https://unsloth.ai/docs/models/qwen3.5/gguf-benchmarks With the Qwen3.5 35B A3B at Q4 I've got 200k context running at 62.98 tokens per second on a local RTX5080 16GB.”
Frequently asked
Quick answers to common questions
What is Unsloth?
Unsloth is a fine-tuning tool for local AI workloads. 2x faster LLM fine-tuning with 50% less memory - supports Llama, Mistral, Gemma, Qwen, and more.
Is Unsloth free and open source?
Yes, Unsloth has 78,230 GitHub stars and is licensed under Apache-2.0. You can self-host it for free on linux, docker.
What platforms does Unsloth support?
Unsloth runs on linux, docker.
What hardware do I need for Unsloth?
The hardware requirements depend on which models you run. Check our hardware directory for compatible GPUs and systems. Unsloth has 78,230 GitHub stars and an active community.
Does Unsloth support GPU acceleration?
Unsloth supports GPU acceleration via CUDA, Metal, or Vulkan depending on your platform. For the best performance, pair it with an NVIDIA RTX 4090 or 5090.
What are the best alternatives to Unsloth?
Popular alternatives include other fine-tuning tools in our directory. Browse our full collection at /tool for comparisons, community reviews, and benchmark data to find the right fit for your workflow.
How much does Unsloth cost?
Unsloth is free-tier. There is a free tier available with paid upgrades for advanced features.
Pairs well with
Complementary tools, models, and hardware