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+This was performed by Trainy team on WhiteFiber in June 2025, to get a baseline of performance 
+of the Trainy platform on H200s platform over multiple hosts.
+
+### Models
+
+Llama 3.1 8B
+
+### Hardware
+
+Each host has
+
+- 8 NVIDIA H200 GPUs connected via NVLink.
+- Hosts are inter-connected with a backend RDMA fabric with 400Gb/s (Mellanox CX-7) per GPU.
+
+### Configuration
+
+Runs were invoked with the following, where `NUM_NODES` was `4` and `8`
+```
+  torchrun \
+    --nnodes $NUM_NODES  \
+    --nproc_per_node 8 \
+    --rdzv_id 101 \
+    --rdzv_backend c10d \
+    --rdzv_endpoint "$MASTER_ADDR:29500" \
+    torchtitan/train.py \
+    --job.config-file torchtitan/models/llama3/train_configs/llama3_8b.toml \
+    --metrics.enable_wandb \
+    --training.local_batch_size=2 \
+    --training.compile \
+    --model.converters="float8" \
+    --float8.enable_fsdp_float8_all_gather \
+    --float8.precompute_float8_dynamic_scale_for_fsdp \
+    --float8.force_recompute_fp8_weight_in_bwd \
+    --profiling.profile_freq 1000000
+    --training.steps 2000
+```
+
+### Results
+
+Detailed performance results and training configurations can be found in the tables below along and can visualized in [this WandB report](https://api.wandb.ai/links/asaiacai/w4c46stp). `TPS` and `Memory(GiB)` are arbitrarily sampled at the 100th iteration:
+
+| NUM_NODES | TPS/GPU | Memory(GiB) |
+| ----- | ----: | ----: |
+| 4 | 10938 | 47.96 |
+| 8 | 10753 | 46.97 |
+
+
+### Versions and Dates
+
+| repo | commit | date |
+| --- | --- | --- |
+| torch | [2.8.0a0+5228986c39](https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel-25-05.html) | 2025/05/29 |
+| torchao | [0afa4c1](https://github.com/pytorch/ao/commit/0afa4c1bd28c82921e360ddbd1b27c9d6da5b947) | 2025/06/13 |
+| torchtitan | [e7c0cae](https://github.com/pytorch/torchtitan/commit/e7c0cae934df78d6e9c2835f42ff1f757dc3fddc) | 2025/06/13 |