add INT8 example into dsv3 README (#4079)

benchmark/deepseek_v3/README.md

@@ -184,6 +184,7 @@ AWQ does not support BF16, so add the `--dtype half` flag if AWQ is used for qua
 python3 -m sglang.launch_server --model cognitivecomputations/DeepSeek-R1-AWQ --tp 8 --trust-remote-code --dtype half
 ```
 
+
 ### Example: Serving with 16 A100/A800 with int8 Quantization
 
 There are block-wise and per-channel quantization methods, and the quantization parameters have already been uploaded to Huggingface. One example is as follows:
@@ -191,18 +192,31 @@ There are block-wise and per-channel quantization methods, and the quantization
 - [meituan/DeepSeek-R1-Block-INT8](https://huggingface.co/meituan/DeepSeek-R1-Block-INT8)
 - [meituan/DeepSeek-R1-Channel-INT8](https://huggingface.co/meituan/DeepSeek-R1-Channel-INT8)
 
+Assuming that master node IP is `MASTER_IP`, checkpoint path is `/path/to/DeepSeek-R1-INT8` and port=5000, we can have following commands to launch the server:
 ```bash
 #master
 python3 -m sglang.launch_server \
 	--model meituan/DeepSeek-R1-Block-INT8 --tp 16 --dist-init-addr \
-	HEAD_IP:5000 --nnodes 2 --node-rank 0 --trust-remote --enable-torch-compile --torch-compile-max-bs 8
+	MASTER_IP:5000 --nnodes 2 --node-rank 0 --trust-remote --enable-torch-compile --torch-compile-max-bs 8
 #cluster
 python3 -m sglang.launch_server \
 	--model meituan/DeepSeek-R1-Block-INT8 --tp 16 --dist-init-addr \
-	HEAD_IP:5000 --nnodes 2 --node-rank 1 --trust-remote --enable-torch-compile --torch-compile-max-bs 8
+	MASTER_IP:5000 --nnodes 2 --node-rank 1 --trust-remote --enable-torch-compile --torch-compile-max-bs 8
 ```
 
+> **Note that the launch command here enables `torch.compile` Optimization**. For optimal performance, please refer to the command options in [Performance Optimization Options](#option_args).
+
+Then on the **master node**, supposing the ShareGPT data is located at `/path/to/ShareGPT_V3_unfiltered_cleaned_split.json`, you can run the following commands to benchmark the launched server:
+
+```bash
+# bench accuracy
+python3 benchmark/gsm8k/bench_sglang.py --num-questions 1319
+
+# bench serving
+python3 -m sglang.bench_serving --dataset-path /path/to/ShareGPT_V3_unfiltered_cleaned_split.json --dataset-name random  --random-input 128 --random-output 128 --num-prompts 1000 --request-rate 128 --random-range-ratio 1.0
+```
 
+> **Note: using `--parallel 200` can accelerate accuracy benchmarking**.
 
 ### Example: Serving on any cloud or Kubernetes with SkyPilot