Several frameworks support LLM training and inference, with PyTorch and TensorFlow being the most widely used. These frameworks provide tools for implementing transformer architectures, managing data pipelines, and optimizing training processes. PyTorch, for example, offers the transformers library through Hugging Face, making it easier to work with pre-trained LLMs like BERT, GPT, and T5.
For inference, frameworks like Hugging Face Transformers simplify deploying LLMs by providing pre-built models and APIs for generating predictions. TensorFlow Serving and ONNX Runtime are also popular for deploying models in production environments, offering scalability and support for various hardware.
Beyond these, specialized tools like DeepSpeed and NVIDIA Triton optimize training and inference for large-scale models. DeepSpeed enables distributed training across multiple GPUs, while Triton accelerates inference with efficient use of GPU resources. These frameworks, combined with cloud services like AWS SageMaker or Google AI Platform, form a robust ecosystem for LLM development and deployment.