#GPU optimization

Horovod facilitates distributed deep learning by easing the transition from single-GPU to multi-GPU and multi-host setups. Utilizing MPI and NCCL, it minimizes code changes while boosting training speed and efficiency. Compatible with TensorFlow and PyTorch, Horovod is easy to install and use, backed by the LF AI & Data Foundation for community and documentation support, ideal for those utilizing open-source AI technologies.

Marlin, an FP16xINT4 optimized kernel, accelerates LLM inference with batch sizes of 16-32 tokens using advanced GPU techniques. It outperforms comparable kernels under various GPU conditions and is easily integrated with CUDA and torch. Key features include asynchronous global weight management and efficient resource allocation.

FlashInfer is a library offering high-performance GPU kernels for Large Language Model serving, including FlashAttention and SparseAttention. It covers diverse applications such as single-request and batch processing using various KV-Cache formats. The library specializes in optimizing shared-prefix batch decoding with cascading techniques, providing up to 31x speedup. FlashInfer supports integration with PyTorch, TVM, and C++ APIs, facilitating efficient memory use and fast deployments in quantized attention contexts for modern LLMs.

Discover NVIDIA's open-source library designed for efficient training of large language models with GPU optimization. Megatron-Core provides modular APIs for enhanced system-level optimization and scalability, supporting multimodal training on NVIDIA infrastructure. Features include advanced parallelism strategies and comprehensive components for transformers such as BERT and GPT, ideal for AI researchers and developers. It integrates smoothly with frameworks like NVIDIA NeMo and PyTorch.