float8 readme: add key features section (#2448)

Adds a section summarizing the key features of float8 training

Author: vkuzo

torchao/float8/README.md

@@ -6,6 +6,15 @@ and up to [**1.25x at 8 GPU / 8B parameter count scale**](#training-benchmarks).
 The codebase strives to stay small, hackable, debuggable with native PyTorch tooling
 and composable with key systems such as autograd, ```torch.compile``` and distributed.
 
+## Key features
+
+* e2e pretraining speedups of up to [**1.5x at 512 GPU / 405B parameter count scale**](https://pytorch.org/blog/training-using-float8-fsdp2/),
+and up to [**1.25x at 8 GPU / 8B parameter count scale**](#training-benchmarks), with performance and accuracy validated on up to [**2k GPUs**](https://pytorch.org/blog/accelerating-large-scale-training-and-convergence-with-pytorch-float8-rowwise-on-crusoe-2k-h200s/), via [torchtitan's float8 integration](https://github.com/pytorch/torchtitan/blob/main/docs/float8.md)
+* seamless composability with [torch.compile](https://docs.pytorch.org/docs/stable/torch.compiler.html)
+* seamless composability with [DTensor](https://docs.pytorch.org/docs/stable/distributed.tensor.html), including [FSDP2 with float8 weight all-gather](https://dev-discuss.pytorch.org/t/enabling-float8-all-gather-in-fsdp2/2359) and [Async TP](https://discuss.pytorch.org/t/distributed-w-torchtitan-introducing-async-tensor-parallelism-in-pytorch/209487)
+* seamless composability with [PyTorch Activation Checkpointing](https://pytorch.org/blog/activation-checkpointing-techniques/)
+* three different scaling recipes to trade off performance vs accuracy: tensorwise (fastest), rowwise, rowwise_with_gw_hp (most accurate)
+
 ℹ️ <em>See the [feature tracker](https://github.com/pytorch/ao/issues/556) for upcoming features.</em>
 
 ℹ️ <em>These APIs are training-only and float8-only, and we plan to [unify them with the rest of torchao](https://github.com/pytorch/ao/issues/894) in the future.</em>