Unsloth
Fine-tune and train LLMs up to 30x faster with 90% less memory
About Unsloth
Unsloth is an open-source platform for fine-tuning and reinforcement learning (RL) on large language models. It dramatically reduces the time and memory needed to fine-tune LLMs through custom-written GPU kernels and handwritten math derivations — achieving up to 30x faster training speeds with 90% less VRAM compared to traditional methods. Unsloth supports 500+ models including Llama 4, DeepSeek-R1, Gemma, Qwen3, Mistral, and OpenAI gpt-oss, with training modes for full fine-tuning, LoRA, QLoRA, pre-training, and reinforcement learning. The platform works on NVIDIA GPUs from Tesla T4 to H100, with portability to AMD and Intel GPUs. Unsloth offers free notebooks for Google Colab and Kaggle, making LLM fine-tuning accessible to individual developers, while Pro and Enterprise tiers unlock multi-GPU and multi-node training for teams at scale.
Pros & Cons
Pros
- Massive speed improvement (up to 30x) makes fine-tuning practical on consumer GPUs
- Dramatically reduced VRAM usage (up to 90%) enables training on smaller hardware
- Free and open source with Apache 2.0 license
- Excellent onboarding with free Colab/Kaggle notebooks — start fine-tuning instantly
- Broad model support covering 500+ LLMs across text, vision, TTS, and embeddings
- No accuracy loss despite speed optimizations — mathematically verified
Cons
- Free tier limited to single GPU — multi-GPU requires paid Pro plan
- Pro and Enterprise pricing not publicly listed (contact sales)
- Requires GPU hardware — no CPU-only training option
- Steeper learning curve for users new to LLM fine-tuning concepts
- Multi-node training only available in Enterprise tier
Key Features
Up to 30x faster LLM fine-tuning through custom GPU kernels
90% less VRAM usage compared to standard training methods
Supports 500+ models: Llama, DeepSeek, Gemma, Qwen, Mistral, and more
Full fine-tuning, LoRA, QLoRA, pre-training, and RL training modes
Most memory-efficient library for reinforcement learning (80% less VRAM)
Free notebooks for Google Colab and Kaggle — zero-cost fine-tuning
Works on NVIDIA (T4 to H100), AMD, and Intel GPUs
Flexible chat templates, dataset formatting, and ready-to-use notebooks
Fine-tuning open-source LLMs for domain-specific tasks
Use case
Reinforcement learning from human feedback (RLHF)
Use case
Training vision, TTS, and embedding models
Use case
Cost-effective model customization on consumer GPUs
Use case
Pre-training custom language models
Use case
Deploying optimized models for faster inference
Use case
Hugging Face Transformers integration
Integration
Google Colab integration
Integration
Kaggle integration
Integration
NVIDIA GPUs integration
Integration
AMD GPUs integration
Integration
Intel GPUs integration
Integration
PyTorch integration
Integration
Pricing
- Open source (Apache 2.0)
- 2x faster training speed
- 60% VRAM reduction
- Single GPU support
- 4-bit and 16-bit LoRA
- 500+ supported models
- Free Colab and Kaggle notebooks
- 2.5x faster training vs FA2
- 80% VRAM reduction
- Multi-GPU support (up to 8 GPUs)
- Enhanced GPU speedup
- All Free tier features included
- 32x number of GPUs faster than FA2
- 90% VRAM reduction
- Multi-node training capabilities
- Up to 30% accuracy improvement
- 5x faster inference
- Full training support
- Dedicated customer support