Unsloth
Fine-tuning & Reinforcement Learning for LLMs. Train models faster with less VRAM.
- Status
- Generally available
- Last checked
- Checked · Oct 16, 2026
- License
- Open source
- Pricing
- Free tier
What it is
Unsloth accelerates the training of large language models like OpenAI gpt-oss, DeepSeek, Qwen, Llama, Gemma, and TTS by up to 2x while reducing VRAM usage by 70%. Ideal for developers looking to optimize their model training processes.
Unsloth stands out by offering a significant speed boost in LLM training while drastically reducing VRAM requirements, making it an ideal choice for developers looking to optimize their computational resources.
When it fits — and when it doesn’t
A good fit for
Developers who need to fine-tune LLMs quickly and efficiently
Teams working on reinforcement learning projects with limited VRAM resources
Researchers looking for a flexible tool to train various types of language models
Look elsewhere if
Projects requiring real-time model training or inference (batch-only architecture)
Budget-constrained projects where the initial setup and maintenance costs are critical
Known weaknesses
Each one links to the public issue, changelog or docs page it came from, where we have one.
- 01
Limited support for non-Python frameworks
Primary language is Python, and other languages are not officially supported
- 02
Performance degradation with certain model types
Some complex models like DeepSeek show less than 2x speedup or no VRAM reduction
What it does well
Accelerates model training up to 2x faster
Reduces VRAM usage by 70%
Supports multiple LLM frameworks including OpenAI, DeepSeek, Qwen, and more
Pricing
- Free tier
- Yes
- Paid from
- —
- Model
- Flat rate
- Enterprise plan
- No
Pricing not independently confirmed — check the vendor’s page.
Performance benchmarks
How Fast Is It?
Set up Unsloth
Setup steps, common gotchas and what to check after 30 days.