diff --git a/torchao/float8/README.md b/torchao/float8/README.md
index 65105d1f89..9e37bb001d 100644
--- a/torchao/float8/README.md
+++ b/torchao/float8/README.md
@@ -202,3 +202,42 @@ python test/float8/test_fsdp2/test_fsdp2.py
 # make sure to turn on torch.compile to get the best performance
 ./benchmarks/float8/bench_linear_float8.py -o ../tmp/test.txt --compile
 ```
+
+### Training benchmarks
+
+[Torchtitan](https://github.com/pytorch/torchtitan) was used to benchmark float8 training performance, for both rowwise
+and tensorwise scaling. The training benchmarks were all run using:
+
+- Single-node training on 8xH100 GPUs
+- Batch size 1
+- Sequence length 8192
+- Steps 100
+- `torch.compile`
+- FSDP2
+- pytorch version: `2.7.0a0+gitb98af95`
+- torchao version: `0.10.0+git890e0ac8`
+- torchtitan version: `0.0.2`
+
+
+| Model         | Scaling                            | Activation checkpointing | Peak Memory (GB)  | Median tokens/second | Speedup over baseline
+| ------------- | ---------------------------------- | ------------------------ | ------------------| -------------------- | ---------------------
+| Llama3-8b     |  none (bfloat16)                   | per op SAC               | 47.65             |  6150                | -
+| Llama3-8b     |  tensorwise with float8 all-gather | per op SAC               | 47.77             |  7689.5              | 25.03%
+| Llama3-8b     |  rowwise with bfloat16 all-gather  | per op SAC               | 47.79             |  6768                | 10.05%
+
+**Important notes**:
+- E2E speedups increase as M,K,N (GEMM dimensions) increase. Speedups as high as 1.5x have been measured with larger shapes ((example)[https://pytorch.org/blog/training-using-float8-fsdp2/]).
+- Rowwise scaling is better at handling outliers than tensorwise scaling, so these recipes are different points on the accuracy vs performance curve.
+
+**Reproducing training benchmarks**
+To reproduce these benchmarks, you can follow these steps:
+
+1. On a machine with 8 H100 GPUs, clone torchtitan and follow local installation [steps](https://github.com/pytorch/torchtitan?tab=readme-ov-file#installation),
+including [downloading a tokenizer](https://github.com/pytorch/torchtitan?tab=readme-ov-file#downloading-a-tokenizer).
+2. Install torchao following these [steps](https://github.com/pytorch/ao/tree/main?tab=readme-ov-file#installation).
+3. From the `torchao/float8/benchmarking/` directory, you can run the following commands to reproduce the benchmarks above:
+   - bf16 + compile: `TORCHTITAN_ROOT=<path> ./float8_training_benchmark.sh`
+   - float8 tensorwise with float8 all-gather + compile: `TORCHTITAN_ROOT=<path> FLOAT8_RECIPE_WITH_BEST_SETTINGS="tensorwise" ./float8_training_benchmark.sh`
+   - float8 rowwise with bf16 all-gather + compile: `TORCHTITAN_ROOT=<path> FLOAT8_RECIPE_WITH_BEST_SETTINGS="rowwise" ./float8_training_benchmark.sh`
+
+See the float8 training benchmarking [guide](.torchao/float8/benchmarking/README.md) for more details.