Several frameworks and libraries support training and inference for large language models (LLMs), each offering distinct features for different stages of the model lifecycle. The most widely used tools include PyTorch, TensorFlow, JAX, Hugging Face Transformers, and specialized optimization libraries like DeepSpeed and vLLM. These frameworks address challenges such as distributed training, memory efficiency, and high-performance inference. Let’s break down their roles and use cases.
For training LLMs, PyTorch and TensorFlow are foundational. PyTorch is favored for its dynamic computation graphs, which simplify debugging and experimentation. Its ecosystem includes libraries like PyTorch Lightning for distributed training and Fully Sharded Data Parallel (FSDP) for memory-efficient scaling. TensorFlow, while less dominant in research today, remains strong in production pipelines, particularly with TensorFlow Extended (TFX) and TPU support. JAX, though less mainstream, is gaining traction for its composable function transformations (e.g., jit, pmap) and scalability, making it ideal for researchers optimizing low-level operations. Libraries like Hugging Face Transformers abstract model implementation, offering pre-trained models (e.g., BERT, GPT-2) and training utilities, while DeepSpeed provides ZeRO optimization and model parallelism to reduce memory overhead during distributed training.
For inference, frameworks prioritize latency and throughput. TensorFlow Serving and PyTorch’s TorchServe are deployment-focused, offering model versioning and batch processing. Specialized tools like vLLM use techniques such as PagedAttention to maximize GPU memory utilization, achieving high throughput for models like LLaMA. ONNX Runtime and NVIDIA’s TensorRT optimize inference via quantization and kernel fusion, reducing compute demands. Hugging Face’s Pipelines API simplifies inference for common tasks, while cloud services (AWS SageMaker, Google Vertex AI) provide managed endpoints. Each tool balances ease of use, hardware compatibility, and performance, letting developers choose based on deployment needs.
In summary, the choice of framework depends on the task: PyTorch and JAX for flexible training, Hugging Face for accessible model access, and vLLM or TensorRT for optimized inference. Combining these tools—like training with PyTorch + DeepSpeed and deploying with vLLM—is common in production pipelines. Understanding their strengths helps developers build efficient workflows for LLM development.