inference-optimization

High-efficiency floating-point neural network inference operators for mobile, server, and Web

BladeDISC is an end-to-end DynamIc Shape Compiler project for machine learning workloads.

The Tensor Algebra SuperOptimizer for Deep Learning

[MLSys 2021] IOS: Inter-Operator Scheduler for CNN Acceleration

Batch normalization fusion for PyTorch

Optimize layers structure of Keras model to reduce computation time

A set of tool which would make your life easier with Tensorrt and Onnxruntime. This Repo is designed for YoloV3

Official Repo for SparseLLM: Global Pruning of LLMs (NeurIPS 2024)

The blog, read report and code example for AGI/LLM related knowledge.

[CVPR 2025] DivPrune: Diversity-based Visual Token Pruning for Large Multimodal Models

cross-platform modular neural network inference library, small and efficient

Learn the ins and outs of efficiently serving Large Language Models (LLMs). Dive into optimization techniques, including KV caching and Low Rank Adapters (LoRA), and gain hands-on experience with Predibase’s LoRAX framework inference server.

Faster inference YOLOv8: Optimize and export YOLOv8 models for faster inference using OpenVINO and Numpy 🔢

A template for getting started writing code using GGML

Accelerating LLM inference with techniques like speculative decoding, quantization, and kernel fusion, focusing on implementing state-of-the-art research papers.

LLM-Rank: A graph theoretical approach to structured pruning of large language models based on weighted Page Rank centrality as introduced by the related paper.

Your AI Catalyst: inference backend to maximize your model's inference performance

A constrained expectation-maximization algorithm for feasible graph inference.

🤖️ Optimized CUDA Kernels for Fast MobileNetV2 Inference

Batch Partitioning for Multi-PE Inference with TVM (2020)