Server Inference

LLM Inference Servers

• GitHub - sgl-project/sglang: SGLang is yet another fast serving framework for large language models and vision language models.
  • GitHub - sgl-project/sgl-learning-materials: Materials for learning SGLang
• GitHub - vllm-project/vllm: A high-throughput and memory-efficient inference and serving engine for LLMs
• GitHub - NVIDIA/TensorRT-LLM: TensorRT-LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and build TensorRT engines that contain state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT-LLM also contains components to create Python and C++ runtimes that execute those TensorRT engines.
• GitHub - flashinfer-ai/flashinfer: FlashInfer: Kernel Library for LLM Serving
  • [2501.01005] FlashInfer: Efficient and Customizable Attention Engine for LLM Inference Serving
• GitHub - InternLM/lmdeploy: LMDeploy is a toolkit for compressing, deploying, and serving LLMs.

Triton-inference-server

• Serving a Torch-TensorRT model with Triton — Torch-TensorRT v1.4.0.dev0+d0af394 documentation

ONNX Runtime

• https://github.com/microsoft/onnxruntime-inference-examples
• https://github.com/microsoft/DeepSpeed-MII
• https://github.com/microsoft/onnx-script

GitHub - open-mmlab/mmdeploy: OpenMMLab Model Deployment Framework