Optimization story: Bloom inference

1. 1Porting code to JAX/Flax for TPU compatibility
2. 2Exploring compiled approaches like ONNX/TRT
3. 3Using DeepSpeed for optimization
4. 4Writing custom PyTorch kernels for efficiency

AI engineers and researchers can benefit from this story to optimize their own models, while product managers can understand the trade-offs involved in deploying large language models

💡 Combining PyTorch with TPUs and custom kernels can lead to significant performance gains

💡 Optimizing Bloom inference with PyTorch, TPUs, and custom kernels