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 ## 📣 Latest News
 
+- [Sept 25] MXFP8 training achieved [1.28x speedup on Crusoe B200 cluster](https://pytorch.org/blog/accelerating-2k-scale-pre-training-up-to-1-28x-with-torchao-mxfp8-and-torchtitan-on-crusoe-b200-cluster/) with virtually identical loss curve to bfloat16!
 - [Jun 25] Our [TorchAO paper](https://openreview.net/attachment?id=HpqH0JakHf&name=pdf) was accepted to CodeML @ ICML 2025!
 - [May 25] QAT is now integrated into [Axolotl](https://github.com/axolotl-ai-cloud/axolotl) for fine-tuning ([docs](https://docs.axolotl.ai/docs/qat.html))!
 - [Apr 25] Float8 rowwise training yielded [1.34-1.43x training speedup](https://pytorch.org/blog/accelerating-large-scale-training-and-convergence-with-pytorch-float8-rowwise-on-crusoe-2k-h200s/) at 2k H100 GPU scale
@@ -50,6 +51,7 @@
 TorchAO is an easy to use quantization library for native PyTorch. TorchAO works out-of-the-box with `torch.compile()` and `FSDP2` across most HuggingFace PyTorch models. Key features include:
 * Float8 [training](torchao/float8/README.md) and [inference](https://docs.pytorch.org/ao/main/generated/torchao.quantization.Float8DynamicActivationFloat8WeightConfig.html) for speedups without compromising accuracy
 * [MX training and inference](torchao/prototype/mx_formats/README.md), provides MX tensor formats based on native PyTorch MX dtypes (prototype)
+* [Low precision MoE training](torchao/prototype/moe_training/README.md) provides training speedups with comparable numerics to bfloat16 training
 * [Quantization-Aware Training (QAT)](torchao/quantization/qat/README.md) for mitigating quantization degradation
 * [Post-Training Quantization (PTQ)](torchao/quantization/README.md) for int4, int8, fp6 etc, with matching kernels targeting a variety of backends including CUDA, ARM CPU, and XNNPACK
 * [Sparsity](torchao/sparsity/README.md), includes different techniques such as 2:4 sparsity and block sparsity