Fix install instructions and pyproject.tomls (#11781)

docs/get_started/install.md

@@ -12,7 +12,7 @@ It is recommended to use uv for faster installation:
 ```bash
 pip install --upgrade pip
 pip install uv
-uv pip install sglang --upgrade
+uv pip install sglang --prerelease=allow
 ```
 
 **Quick fixes to common problems**
@@ -129,5 +129,3 @@ sky status --endpoint 30000 sglang
 
 - [FlashInfer](https://github.com/flashinfer-ai/flashinfer) is the default attention kernel backend. It only supports sm75 and above. If you encounter any FlashInfer-related issues on sm75+ devices (e.g., T4, A10, A100, L4, L40S, H100), please switch to other kernels by adding `--attention-backend triton --sampling-backend pytorch` and open an issue on GitHub.
 - To reinstall flashinfer locally, use the following command: `pip3 install --upgrade flashinfer-python --force-reinstall --no-deps` and then delete the cache with `rm -rf ~/.cache/flashinfer`.
-- If you only need to use OpenAI API models with the frontend language, you can avoid installing other dependencies by using `pip install "sglang[openai]"`.
-- The language frontend operates independently of the backend runtime. You can install the frontend locally without needing a GPU, while the backend can be set up on a GPU-enabled machine. To install the frontend, run `pip install sglang`, and for the backend, use `pip install sglang[srt]`. `srt` is the abbreviation of SGLang runtime.

docs/index.rst

@@ -1,14 +1,15 @@
 SGLang Documentation
 ====================
 
-SGLang is a fast serving framework for large language models and vision language models.
-It makes your interaction with models faster and more controllable by co-designing the backend runtime and frontend language.
-The core features include:
+SGLang is a high-performance serving framework for large language models and vision-language 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, 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.
-- **Flexible Frontend Language**: Offers an intuitive interface for programming LLM applications, including chained generation calls, advanced prompting, control flow, multi-modal inputs, parallelism, and external interactions.
-- **Extensive Model Support**: Supports a wide range of generative models (Llama, Qwen, DeepSeek, Kimi, GPT, Gemma, Mistral, etc.), embedding models (e5-mistral, gte, mcdse) and reward models (Skywork), with easy extensibility for integrating new models.
-- **Active Community**: SGLang is open-source and backed by an active community with wide industry adoption.
+- **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), and reward models (Skywork), with easy extensibility for integrating 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 300,000 GPUs worldwide.
 
 .. toctree::
    :maxdepth: 1

docs/platforms/tpu.md

@@ -1,3 +1,3 @@
 # TPU
 
-The support for TPU is under active development. Please stay tuned.
+See https://github.com/sgl-project/sglang-jax