diff --git a/README.md b/README.md
index dbf88ba..1b8a59c 100644
--- a/README.md
+++ b/README.md
@@ -227,7 +227,7 @@ We also provide OpenAI-Compatible API at DeepSeek Platform: [platform.deepseek.c
 DeepSeek-V3 can be deployed locally using the following hardware and open-source community software:
 
 1. **DeepSeek-Infer Demo**: We provide a simple and lightweight demo for FP8 and BF16 inference.
-2. **SGLang**: Fully support the DeepSeek-V3 model in both BF16 and FP8 inference modes.
+2. **SGLang**: Fully support the DeepSeek-V3 model in both BF16 and FP8 inference modes, with Multi-Token Prediction [coming soon](https://github.com/sgl-project/sglang/issues/2591).
 3. **LMDeploy**: Enables efficient FP8 and BF16 inference for local and cloud deployment.
 4. **TensorRT-LLM**: Currently supports BF16 inference and INT4/8 quantization, with FP8 support coming soon.
 5. **vLLM**: Support DeekSeek-V3 model with FP8 and BF16 modes for tensor parallelism and pipeline parallelism.
@@ -288,10 +288,14 @@ torchrun --nnodes 2 --nproc-per-node 8 generate.py --node-rank $RANK --master-ad
 
 ### 6.2 Inference with SGLang (recommended)
 
-[SGLang](https://github.com/sgl-project/sglang) currently supports MLA optimizations, FP8 (W8A8), FP8 KV Cache, and Torch Compile, delivering state-of-the-art latency and throughput performance among open-source frameworks.
+[SGLang](https://github.com/sgl-project/sglang) currently supports [MLA optimizations](https://lmsys.org/blog/2024-09-04-sglang-v0-3/#deepseek-multi-head-latent-attention-mla-throughput-optimizations), [DP Attention](https://lmsys.org/blog/2024-12-04-sglang-v0-4/#data-parallelism-attention-for-deepseek-models), FP8 (W8A8), FP8 KV Cache, and Torch Compile, delivering state-of-the-art latency and throughput performance among open-source frameworks.
 
 Notably, [SGLang v0.4.1](https://github.com/sgl-project/sglang/releases/tag/v0.4.1) fully supports running DeepSeek-V3 on both **NVIDIA and AMD GPUs**, making it a highly versatile and robust solution.
 
+SGLang also supports [multi-node tensor parallelism](https://github.com/sgl-project/sglang/tree/main/benchmark/deepseek_v3#example-serving-with-2-h208), enabling you to run this model on multiple network-connected machines.
+
+Multi-Token Prediction (MTP) is in development, and progress can be tracked in the [optimization plan](https://github.com/sgl-project/sglang/issues/2591).
+
 Here are the launch instructions from the SGLang team: https://github.com/sgl-project/sglang/tree/main/benchmark/deepseek_v3
 
 ### 6.3 Inference with LMDeploy (recommended)