#Inference Acceleration

Discover a curated list of cutting-edge research papers on improving the efficiency of Large Language Models (LLMs) through methods such as network pruning, knowledge distillation, and quantization. This resource provides insights into accelerating inference, optimizing architectures, and enhancing hardware performance, offering valuable information for both academic and industry professionals.

Bolt, a library for efficient neural network deployment, offers over 15% speed improvement compared to other open-source libraries. It facilitates model conversion from Caffe, ONNX, TFLite, and TensorFlow, supporting multiple inference precisions such as FP32 and 1-BIT. Bolt's compatibility spans various platforms, including ARM and X86 CPUs and GPUs from diverse vendors. It pioneers support for NLP alongside CV applications, featuring advanced graph optimization, efficient thread settings, and algorithm tuning. Extensively used in HUAWEI departments, Bolt is a reliable tool enhancing performance in CV, NLP, and recommendation applications.

Explore a comprehensive library of leading-edge papers and code focused on large language model (LLM) inference. It includes topics such as distributed computing, caching methods, and quantization techniques like FP8 and WINT8/4. Designed for researchers and practitioners aiming to improve the efficiency of LLM deployment. The resource covers subjects like Continuous Batching and Parallel Decoding, alongside advancements such as Mooncake and LLM-Viewer. Stay informed on the latest innovations to support your LLM applications.