[Docs] Explain CUDA attention backend choices and aiter FP8 KV cache support (#17428)

docs/advanced_features/attention_backend.md

@@ -25,7 +25,7 @@ The support matrix is split into two parts: MHA (standard attention) and MLA (mu
 | **FlexAttention (PyTorch)**     | ❌                          | ❌               | ✅              | ❌              | ❌              | ❌                 | ❌             |
 | **TRTLLM MHA**                  | 16, 32 or 64                | ✅               | ✅              | ✅              | ❌              | ✅                 | ❌             |
 | **Dual Chunk FlashAttention**   | ✅                          | ❌               | ❌              | ❌              | ❌              | ❌                 | ❌             |
-| **AITER (ROCm)**                | ✅                          | ❌               | ❌              | ✅              | ✅              | ❌                 | ✅             |
+| **AITER (ROCm)**                | ✅                          | ✅               | ❌              | ✅              | ✅              | ❌                 | ✅             |
 | **Wave (ROCm)**                 | ✅                          | ❌               | ❌              | ❌              | ❌              | ❌                 | ❌             |
 | **Ascend (NPU)**                | ✅                          | ❌               | ❌              | ❌              | ❌              | ❌                 | ✅             |
 | **Intel XPU**                   | ✅                          | ❌               | ❌              | ❌              | ❌              | ✅                 | ❌             |
@@ -111,6 +111,23 @@ If you set only one of `--prefill-attention-backend` or `--decode-attention-back
 If both are specified and differ, SGLang automatically enables a hybrid wrapper to dispatch to the chosen backend per phase.
 ```
 
+## Attention Backend Selection Guide (CUDA)
+
+If the `--attention-backend` argument is not specified, SGLang automatically selects the best backend based on the hardware (CUDA) and model architecture.
+
+### Automatic Selection Logic
+
+**1. MHA Models (e.g., Llama, Qwen)**
+- **Hopper (e.g., H100, H200)**: Defaults to `fa3` if using CUDA 12.3+ and the model configuration is supported.
+- **Blackwell (e.g., B200)**: Defaults to `trtllm_mha`, unless using speculative decoding with `topk > 1`.
+- **Other Architectures (Ampere, Ada, etc.)**: Defaults to `flashinfer` if available; otherwise falls back to `triton`.
+
+**2. MLA Models (e.g., DeepSeek V3)**
+- **Hopper**: Defaults to `fa3` (requires CUDA 12.3+).
+- **Blackwell**: Defaults to `trtllm_mla`.
+- **Other Architectures**: Defaults to `triton`.
+
+
 ## User Guide
 
 ### Launch Command for Different Attention Backends