1. Model Optimization: DeepSpeed is a library built by Microsoft that focuses on optimizing large-scale model training for deep learning tasks, while TensorFlow Lite is a light-weight library designed by Google for deploying machine learning models on mobile and embedded devices. DeepSpeed offers various techniques like gradient accumulation, dynamic loss scaling, and 1-bit Adam to enhance training efficiency, while TensorFlow Lite provides tools for quantization, weight pruning, and model compression to reduce model size and inference time for edge devices.

2. Supported Models: DeepSpeed is primarily focused on improving the training performance of large deep learning models, including transformers and language models, by overcoming limitations in memory and compute resources. In contrast, TensorFlow Lite supports a wider range of models beyond deep learning, such as traditional machine learning models, including regression and classification algorithms, for deployment on resource-constrained devices.

3. Parallelism Strategies: DeepSpeed integrates various parallelism strategies like data parallelism, model parallelism, and pipeline parallelism to distribute computation and optimize the training process across multiple GPUs or distributed systems. On the other hand, TensorFlow Lite offers tools for converting and deploying models with quantization and post-training quantization techniques to enable efficient inference on devices with limited computational capabilities.

4. Hardware Support: DeepSpeed focuses on optimizing training performance on GPUs and distributed systems, leveraging NVIDIA's CUDA and NCCL libraries for accelerated computation and communication. In contrast, TensorFlow Lite is designed to deploy models on a diverse range of hardware platforms, including CPUs, GPUs, and specialized accelerators like Edge TPUs and Qualcomm Hexagon DSPs, enabling efficient inference for a variety of edge devices.

5. Community and Ecosystem: DeepSpeed is part of the Microsoft ecosystem and provides integrations with PyTorch for enhanced deep learning capabilities, benefiting from research collaborations and contributions from the open-source community. TensorFlow Lite, being a part of the TensorFlow ecosystem, benefits from a large user base, extensive documentation, and support for a wide range of pre-trained models and tools for on-device machine learning tasks.

In Summary, DeepSpeed focuses on optimizing large-scale model training on GPUs with parallelism strategies, while TensorFlow Lite is tailored for deploying machine learning models on mobile and edge devices with quantization and hardware support.