diff --git a/docs/float8.md b/docs/float8.md
index ad481c5e..ccfd7fbd 100644
--- a/docs/float8.md
+++ b/docs/float8.md
@@ -16,3 +16,19 @@ CONFIG_FILE="./train_configs/llama3_8b.toml" ./run_llama_train.sh --float8.enabl
 For parallelisms, we support float8 all-gather for FSDP (optional) and for TP (by default for `Float8Linear`).
 
 For scaling strategy, we currently support tensor-wise scaling with dynamic scales, and are actively working on tensor-wise scaling with delayed scales. Row-wise scaling is under exploration.
+
+## Why Composing Float8 with `torch.distributed`
+**Float8 vs Bfloat16/Float32**: In float8 E4M3 format, we only have 3 bits for mantissa, it becomes user's responsibility to maintain consistent scales across operations (summation, multiplication) to balance between precision and range. For bfloat16/float32, exponent range is large enough and users do not need to maintain such scales. When using float8 in FSDP and TP, tensors are sharded across ranks. To keep single device semantics, it's critical to communicate scales across ranks.
+
+As shown below, for float8 for matmul, `torch._scaled_mm` requires both float8 tensors and their scales. Scales are calculated from `max(abs)` of a high precision tensor.
+```
+# float32/bfloat16 matmul, `torch.mm(input, weight)`, does not require scales
+# float8 matmul requires scales to ensure values to fit within the representable range
+torch._scaled_mm(input_fp8, weight_fp8, scale_a=scale_input, scale_b=scale_weight)
+```
+
+For single device training, we cast input and weight into float8 inside forward before calling `torch._scaled_mm`.
+
+For FSDP, weights are sharded across ranks. We cast high precision weights (1/N on each rank) into float8, and perform float8 all-gather to save bandwidth. At the beginning of the forward, we already have the unsharded float8 weights. The overhead is communicating `max(abs)` across ranks. Float8 all-gather and amax communication can be a net win over float32/bfloat16 all-gather, depending on world size and message size.
+
+For TP, a typical example is row-wise sharded input and column-wise sharded weight. For input, we cast sharded input into float8 and perform float8 all-gather for unsharded input. The overhead is communicating `max(abs)` across ranks. For sharded weights, we communicate `max(abs)` as well. Inside the forward, we perform matmul with float8 input (unsharded) and float8 weight (sharded) with their global `max(abs)`.