Here are seven popular frameworks for serving LLMs (Large Language Models):
TensorFlow Serving: Developed by Google, TensorFlow Serving is a high-performance serving system designed for deploying machine learning models. It allows you to serve TensorFlow models, including LLMs, with low latency and high throughput.
ONNX Runtime: ONNX Runtime is an open-source runtime for ONNX (Open Neural Network Exchange) models. It supports deep learning frameworks, including PyTorch and TensorFlow, and can serve LLMs in production environments.
TorchServe: TorchServe is a model serving library for PyTorch models. It simplifies the deployment of PyTorch-based LLMs, providing features like model versioning, multi-model serving, and monitoring.
FastAPI: Although not a specific framework for serving LLMs, FastAPI is a popular Python web framework known for its high performance and ease of use. It can be used alongside frameworks like TensorFlow or PyTorch to build efficient and scalable LLM-serving APIs.
Triton Inference Server: Developed by NVIDIA, the Triton Inference Server is an open-source inference serving solution that supports various deep learning frameworks, including TensorFlow, PyTorch, and ONNX. It is optimized for GPU-based inference and is suitable for serving LLMs on NVIDIA GPUs.
Clipper: Clipper is an open-source model-serving framework designed to facilitate the deployment of machine learning models at scale. It supports many ML libraries, including TensorFlow and PyTorch, making it suitable for serving LLMs.
OpenVINO: Intel's OpenVINO (Open Visual Inference and Neural Network Optimization) toolkit allows you to optimize and deploy deep learning models, including LLMs, on Intel hardware like CPUs and GPUs. It can be used for high-performance inference in production environments.
These frameworks provide different features and optimizations, so the choice of framework depends on factors like the specific LLM model, hardware, and scalability requirements.