Unsloth
Unsloth AI aims to make AI more accessible to everyone by providing tools that make LLM training and fine-tuning significantly faster and more memory-efficient.
At a Glance
- AI Research Labs
- Enterprise Data Science Teams
- Independent AI Developers
AI Tools by Unsloth
(2)
Unsloth Studio
No Code LLM Fine Tuning UI
Unsloth
LLM Fine Tuning Acceleration
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Products & Services
Fast fine-tuning engine for LLMs on single GPUs, supporting 4-bit and 16-bit LoRA with 2x speedup.
Paid tier offering 2.5x faster training and 20% less VRAM usage compared to the open-source version, with multi-GPU support.
Top-tier offering with 30x faster training, multi-node support, and 90% less memory usage.
A no-code web UI for training, running, and exporting open models in one unified local interface.
Market Position
Unsloth positions itself as the fastest and most efficient LLM fine-tuning library, outperforming standard implementations like Hugging Face PEFT by up to 30x in speed and 90% in memory savings.
Leadership
Founders
Daniel Han
CEO of Unsloth AI. Previously a Machine Learning Engineer at NVIDIA (2018-2020), Research Project Lead at Telstra, and Engineering Project Lead at Westfield. Co-creator of HyperLearn, used by Microsoft and NVIDIA.
Michael Han
CTO of Unsloth AI. Background in Design, Product, and Engineering. Co-creator of HyperLearn. Focuses on high-performance GPU kernels and product design.
Executive Team
Daniel Han
CEO
Ex-NVIDIA ML Engineer, co-creator of HyperLearn.
Michael Han
CTO
Product and Engineering lead, co-creator of HyperLearn.
Board of Directors
Founding Story
Started as a team of two brothers, Daniel and Michael Han, who wanted to solve the massive memory and time constraints of LLM training. Leveraging their experience with the HyperLearn library and Daniel's time at NVIDIA, they built a highly optimized engine using custom CUDA kernels.
Business Model
Revenue Model
Open-core/Freemium model. Revenue is generated through paid subscriptions for Unsloth Pro and Enterprise licenses, offering advanced features like multi-node training and dedicated support.
Pricing Tiers
Single-GPU support, 2x speedup, Mistral/Gemma/Llama support.
Multi-GPU support (up to 8), 2.5x speedup, 20% less VRAM than OSS.
Multi-node support, 30x speedup, 90% less memory, 5x faster inference, priority support.
Target Markets
- AI Research Labs
- Enterprise Data Science Teams
- Independent AI Developers
- Customizing LLMs for vertical-specific applications
- Training high-performance models on consumer-grade hardware
- Enterprise-scale fine-tuning on multi-node clusters
- Research and development of new RLHF and RL methods
- Users from Microsoft
- NVIDIA
- Meta