diff --git a/README.md b/README.md
index f40fda9d9..2f5e5d24f 100644
--- a/README.md
+++ b/README.md
@@ -51,14 +51,13 @@
## About
-SGLang is a high-performance serving framework for large language models and vision-language models.
+SGLang is a high-performance serving framework for large language models and multimodal models.
It is designed to deliver low-latency and high-throughput inference across a wide range of setups, from a single GPU to large distributed clusters.
Its core features include:
-- **Fast Backend Runtime**: Provides efficient serving with RadixAttention for prefix caching, a zero-overhead CPU scheduler, prefill-decode disaggregation, speculative decoding, continuous batching, paged attention, tensor/pipeline/expert/data parallelism, structured outputs, chunked prefill, quantization (FP4/FP8/INT4/AWQ/GPTQ), and multi-LoRA batching.
-- **Extensive Model Support**: Supports a wide range of generative models (Llama, Qwen, DeepSeek, Kimi, GLM, GPT, Gemma, Mistral, etc.), embedding models (e5-mistral, gte, mcdse), reward models (Skywork), and diffusion models (WAN, Qwen-Image), with easy extensibility for integrating new models. Compatible with most Hugging Face models and OpenAI APIs.
+- **Fast Runtime**: Provides efficient serving with RadixAttention for prefix caching, a zero-overhead CPU scheduler, prefill-decode disaggregation, speculative decoding, continuous batching, paged attention, tensor/pipeline/expert/data parallelism, structured outputs, chunked prefill, quantization (FP4/FP8/INT4/AWQ/GPTQ), and multi-LoRA batching.
+- **Broad Model Support**: Supports a wide range of language models (Llama, Qwen, DeepSeek, Kimi, GLM, GPT, Gemma, Mistral, etc.), embedding models (e5-mistral, gte, mcdse), reward models (Skywork), and diffusion models (WAN, Qwen-Image), with easy extensibility for adding new models. Compatible with most Hugging Face models and OpenAI APIs.
- **Extensive Hardware Support**: Runs on NVIDIA GPUs (GB200/B300/H100/A100/Spark), AMD GPUs (MI355/MI300), Intel Xeon CPUs, Google TPUs, Ascend NPUs, and more.
-- **Flexible Frontend Language**: Offers an intuitive interface for programming LLM applications, supporting chained generation calls, advanced prompting, control flow, multi-modal inputs, parallelism, and external interactions.
- **Active Community**: SGLang is open-source and supported by a vibrant community with widespread industry adoption, powering over 400,000 GPUs worldwide.
## Getting Started
diff --git a/docs/README.md b/docs/README.md
index 2cad1335c..f4cb9ce46 100644
--- a/docs/README.md
+++ b/docs/README.md
@@ -1,5 +1,7 @@
# SGLang Documentation
+This is the documentation website for the SGLang project (https://github.com/sgl-project/sglang).
+
We recommend new contributors start from writing documentation, which helps you quickly understand SGLang codebase.
Most documentation files are located under the `docs/` folder.
diff --git a/docs/index.rst b/docs/index.rst
index 6914d7388..832ce9172 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -1,14 +1,20 @@
SGLang Documentation
====================
-SGLang is a high-performance serving framework for large language models and vision-language models.
+.. raw:: html
+
+ Star
+ Fork
+
+
+
+SGLang is a high-performance serving framework for large language models and multimodal models.
It is designed to deliver low-latency and high-throughput inference across a wide range of setups, from a single GPU to large distributed clusters.
Its core features include:
-- **Fast Backend Runtime**: Provides efficient serving with RadixAttention for prefix caching, a zero-overhead CPU scheduler, prefill-decode disaggregation, speculative decoding, continuous batching, paged attention, tensor/pipeline/expert/data parallelism, structured outputs, chunked prefill, quantization (FP4/FP8/INT4/AWQ/GPTQ), and multi-LoRA batching.
-- **Extensive Model Support**: Supports a wide range of generative models (Llama, Qwen, DeepSeek, Kimi, GLM, GPT, Gemma, Mistral, etc.), embedding models (e5-mistral, gte, mcdse), reward models (Skywork), and diffusion models (WAN, Qwen-Image), with easy extensibility for integrating new models. Compatible with most Hugging Face models and OpenAI APIs.
+- **Fast Runtime**: Provides efficient serving with RadixAttention for prefix caching, a zero-overhead CPU scheduler, prefill-decode disaggregation, speculative decoding, continuous batching, paged attention, tensor/pipeline/expert/data parallelism, structured outputs, chunked prefill, quantization (FP4/FP8/INT4/AWQ/GPTQ), and multi-LoRA batching.
+- **Broad Model Support**: Supports a wide range of language models (Llama, Qwen, DeepSeek, Kimi, GLM, GPT, Gemma, Mistral, etc.), embedding models (e5-mistral, gte, mcdse), reward models (Skywork), and diffusion models (WAN, Qwen-Image), with easy extensibility for adding new models. Compatible with most Hugging Face models and OpenAI APIs.
- **Extensive Hardware Support**: Runs on NVIDIA GPUs (GB200/B300/H100/A100/Spark), AMD GPUs (MI355/MI300), Intel Xeon CPUs, Google TPUs, Ascend NPUs, and more.
-- **Flexible Frontend Language**: Offers an intuitive interface for programming LLM applications, supporting chained generation calls, advanced prompting, control flow, multi-modal inputs, parallelism, and external interactions.
- **Active Community**: SGLang is open-source and supported by a vibrant community with widespread industry adoption, powering over 400,000 GPUs worldwide.
.. toctree::