Daniel Han
Unsloth AI
Co-founder and Chief Scientist at Unsloth AI. Pioneered manually derived mathematical backward-pass GPU kernels in OpenAI Triton, enabling 80% lower VRAM usage and 2-5x faster training speeds for fine-tuning Llama, Mistral, Gemma, and DeepSeek foundation models on single GPUs.
Areas of focus
Professional niches
Proof of Work
Manual Backpropagation Triton Kernel Derivations for Parameter-Efficient Cross-Entropy
Published mathematical explanations and Triton code demonstrating how computing gradient chain rules directly inside custom SRAM kernels avoids materializing multi-gigabyte intermediate tensors in PyTorch.
Custom Triton kernels require deep mathematical derivations for each distinct attention and normalization layer.
Context: Triton cross-entropy loss, RoPE embedding kernels, RMSNorm gradient fusion.
View missionUnsloth: Fast and Memory-Efficient Open-Source LLM Fine-Tuning
Created an open-source library that rewrites PyTorch forward and backward passes into handwritten Triton kernels, supporting LoRA, QLoRA, and full fine-tuning with 0% accuracy loss while preventing out-of-memory errors.
Heavily optimized for NVIDIA Ampere, Ada Lovelace, and Hopper architectures; AMD ROCm support is emerging.
Context: Llama 3.1/3.2, Mistral NeMo, DeepSeek-R1-Distill, Gemma 2; 4-bit bitsandbytes and 16-bit mixed precision.
View mission