Custom Triton Kernel Compilation For Attention And Quantization Operations

2x faster LLM fine-tuning with 80% less memory — optimized QLoRA kernels for consumer GPUs.

Unique: Hand-tuned Triton kernels with hardware-aware dispatch system that automatically selects optimal kernel variants based on GPU architecture and model configuration, rather than relying on generic CUDA libraries or PyTorch's default implementations. Includes specialized kernels for grouped query attention, paged attention, and FP8 quantization that are not available in standard frameworks.

vs others: Faster than standard PyTorch/HuggingFace training by 2-5x because custom kernels fuse multiple operations and eliminate redundant memory transfers, whereas generic frameworks execute separate kernels for each operation with full memory round-trips between them.