[Docs] Remove references to `VLLM_ATTENTION_BACKEND` (#30564)

Signed-off-by: Matthew Bonanni <mbonanni@redhat.com>

docs/getting_started/quickstart.md

@@ -281,17 +281,27 @@ Alternatively, you can use the `openai` Python package:
 
 Currently, vLLM supports multiple backends for efficient Attention computation across different platforms and accelerator architectures. It automatically selects the most performant backend compatible with your system and model specifications.
 
-If desired, you can also manually set the backend of your choice by configuring the environment variable `VLLM_ATTENTION_BACKEND` to one of the following options:
+If desired, you can also manually set the backend of your choice using the `--attention-backend` CLI argument:
+
+```bash
+# For online serving
+vllm serve Qwen/Qwen2.5-1.5B-Instruct --attention-backend FLASH_ATTN
+
+# For offline inference
+python script.py --attention-backend FLASHINFER
+```
+
+Some of the available backend options include:
 
 - On NVIDIA CUDA: `FLASH_ATTN` or `FLASHINFER`.
 - On AMD ROCm: `TRITON_ATTN`, `ROCM_ATTN`, `ROCM_AITER_FA` or `ROCM_AITER_UNIFIED_ATTN`.
 
-For AMD ROCm, you can further control the specific Attention implementation using the following variables:
+For AMD ROCm, you can further control the specific Attention implementation using the following options:
 
-- Triton Unified Attention: `VLLM_ROCM_USE_AITER=0 VLLM_V1_USE_PREFILL_DECODE_ATTENTION=0 VLLM_ROCM_USE_AITER_MHA=0`
-- AITER Unified Attention: `VLLM_ROCM_USE_AITER=1 VLLM_USE_AITER_UNIFIED_ATTENTION=1 VLLM_V1_USE_PREFILL_DECODE_ATTENTION=0 VLLM_ROCM_USE_AITER_MHA=0`
-- Triton Prefill-Decode Attention: `VLLM_ROCM_USE_AITER=1 VLLM_V1_USE_PREFILL_DECODE_ATTENTION=1 VLLM_ROCM_USE_AITER_MHA=0`
-- AITER Multi-head Attention: `VLLM_ROCM_USE_AITER=1 VLLM_V1_USE_PREFILL_DECODE_ATTENTION=0 VLLM_ROCM_USE_AITER_MHA=1`
+- Triton Unified Attention: Set the environment variables `VLLM_ROCM_USE_AITER=0 VLLM_ROCM_USE_AITER_MHA=0` and pass `--attention-config.use_prefill_decode_attention=false` as a CLI argument.
+- AITER Unified Attention: Set the environment variables `VLLM_ROCM_USE_AITER=1 VLLM_USE_AITER_UNIFIED_ATTENTION=1 VLLM_ROCM_USE_AITER_MHA=0` and pass `--attention-config.use_prefill_decode_attention=false` as a CLI argument.
+- Triton Prefill-Decode Attention: Set the environment variables `VLLM_ROCM_USE_AITER=1 VLLM_ROCM_USE_AITER_MHA=0` and pass `--attention-config.use_prefill_decode_attention=true` as a CLI argument.
+- AITER Multi-head Attention: Set the environment variables `VLLM_ROCM_USE_AITER=1 VLLM_ROCM_USE_AITER_MHA=1` and pass `--attention-config.use_prefill_decode_attention=false` as a CLI argument.
 
 !!! warning
     There are no pre-built vllm wheels containing Flash Infer, so you must install it in your environment first. Refer to the [Flash Infer official docs](https://docs.flashinfer.ai/) or see [docker/Dockerfile](../../docker/Dockerfile) for instructions on how to install it.