Light-Kernel

Light-Kernel is a high-performance CUDA kernel library powering the LightLLM inference system. It provides optimized GPU implementations for critical operations in large language model (LLM) inference, delivering significant performance improvements through carefully crafted CUDA kernels.

Project Overview

LightLLM-Kernel serves as the computational backbone for LightLLM framework, offering:

• Custom CUDA Kernels: Highly optimized implementations for transformer-based model operations
• Memory Efficiency: Reduced memory footprint through advanced quantization techniques
• Scalability: Support for large model architectures including MoE (Mixture-of-Experts) models

Key Features

Core Modules

Module	Description
Attention	Optimized Multi-Head Attention kernels with fused QKV operations and efficient softmax
MoE	Expert routing and computation kernels for Mixture-of-Experts architectures
Quant	Low-precision quantization support (INT8/INT4) for weights and activations
Extensions	Continuous expansion of optimized operations for emerging model architectures

Installation

System Requirements

• NVIDIA GPU with Compute Capability ≥ 7.0 (Volta+)
• CUDA 11.8 or higher
• Python 3.8+

Installation Methods

Static Compilation (Recommended)

pip install -v .

Build only a wheel package

python -m build --wheel