[Minor] update docs (#14212)

docs/README.md

@@ -51,5 +51,5 @@ pre-commit run --all-files
 - Keep in mind the documentation execution time when writing interactive Jupyter notebooks. Each interactive notebook will be run and compiled against every commit to ensure they are runnable, so it is important to apply some tips to reduce the documentation compilation time:
   - Use small models (e.g., `qwen/qwen2.5-0.5b-instruct`) for most cases to reduce server launch time.
   - Reuse the launched server as much as possible to reduce server launch time.
-- Do not use absolute links (e.g., `https://docs.sglang.ai/get_started/install.html`). Always prefer relative links (e.g., `../get_started/install.md`).
+- Do not use absolute links (e.g., `https://docs.sglang.io/get_started/install.html`). Always prefer relative links (e.g., `../get_started/install.md`).
 - Follow the existing examples to learn how to launch a server, send a query and other common styles.

docs/advanced_features/hicache_design.md

@@ -99,7 +99,7 @@ However, because GPU KV computation is naturally performed layer by layer, the G
 
 ### Integration with PD-Disaggregation Deployment Mode
 
-SGLang supports a PD (Prefill-Decode) disaggregation deployment mode through the Mooncake TransferEngine (for details, see [this doc](https://docs.sglang.ai/advanced_features/pd_disaggregation.html)). In the PD-disaggregation deployment mode, HiCache can be enabled on both the prefill nodes and decode nodes to optimize prefill performance. If enabled on decode nodes, the decode output will also be written back to L3.
+SGLang supports a PD (Prefill-Decode) disaggregation deployment mode through the Mooncake TransferEngine (for details, see [this doc](https://docs.sglang.io/advanced_features/pd_disaggregation.html)). In the PD-disaggregation deployment mode, HiCache can be enabled on both the prefill nodes and decode nodes to optimize prefill performance. If enabled on decode nodes, the decode output will also be written back to L3.
 
 ### Unified Interfaces and Rich L3 Storage Backends
 

docs/advanced_features/pd_disaggregation.md

@@ -19,7 +19,7 @@ Currently, we support Mooncake and NIXL as the transfer engine.
 
 ## Profiling in PD Disaggregation Mode
 
-When you need to profile prefill or decode workers in PD disaggregation mode, please refer to the [Profile In PD Disaggregation Mode](https://docs.sglang.ai/developer_guide/benchmark_and_profiling.html#profile-in-pd-disaggregation-mode) section in the Benchmark and Profiling guide. Due to torch profiler limitations, prefill and decode workers must be profiled separately using dedicated command-line options.
+When you need to profile prefill or decode workers in PD disaggregation mode, please refer to the [Profile In PD Disaggregation Mode](https://docs.sglang.io/developer_guide/benchmark_and_profiling.html#profile-in-pd-disaggregation-mode) section in the Benchmark and Profiling guide. Due to torch profiler limitations, prefill and decode workers must be profiled separately using dedicated command-line options.
 
 ## Router Integration
 

docs/advanced_features/server_arguments.md

@@ -51,7 +51,7 @@ You can find all arguments by `python3 -m sglang.launch_server --help`
   python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --chunked-prefill-size 4096
   ```
 
-- To enable `torch.compile` acceleration, add `--enable-torch-compile`. It accelerates small models on small batch sizes. By default, the cache path is located at `/tmp/torchinductor_root`, you can customize it using environment variable `TORCHINDUCTOR_CACHE_DIR`. For more details, please refer to [PyTorch official documentation](https://pytorch.org/tutorials/recipes/torch_compile_caching_tutorial.html) and [Enabling cache for torch.compile](https://docs.sglang.ai/references/torch_compile_cache.html).
+- To enable `torch.compile` acceleration, add `--enable-torch-compile`. It accelerates small models on small batch sizes. By default, the cache path is located at `/tmp/torchinductor_root`, you can customize it using environment variable `TORCHINDUCTOR_CACHE_DIR`. For more details, please refer to [PyTorch official documentation](https://pytorch.org/tutorials/recipes/torch_compile_caching_tutorial.html) and [Enabling cache for torch.compile](https://docs.sglang.io/references/torch_compile_cache.html).
 - To enable torchao quantization, add `--torchao-config int4wo-128`. It supports other [quantization strategies (INT8/FP8)](https://github.com/sgl-project/sglang/blob/v0.3.6/python/sglang/srt/server_args.py#L671) as well.
 - To enable fp8 weight quantization, add `--quantization fp8` on a fp16 checkpoint or directly load a fp8 checkpoint without specifying any arguments.
 - To enable fp8 kv cache quantization, add `--kv-cache-dtype fp8_e5m2`.

docs/basic_usage/deepseek_v3.md

@@ -153,7 +153,7 @@ python3 -m sglang.compile_deep_gemm --model deepseek-ai/DeepSeek-V3 --tp 8 --tru
 The precompilation process typically takes around 10 minutes to complete.
 
 ### Multi-token Prediction
-**Description**: SGLang implements DeepSeek V3 Multi-Token Prediction (MTP) based on [EAGLE speculative decoding](https://docs.sglang.ai/advanced_features/speculative_decoding.html#EAGLE-Decoding). With this optimization, the decoding speed can be improved by **1.8x** for batch size 1 and **1.5x** for batch size 32 respectively on H200 TP8 setting.
+**Description**: SGLang implements DeepSeek V3 Multi-Token Prediction (MTP) based on [EAGLE speculative decoding](https://docs.sglang.io/advanced_features/speculative_decoding.html#EAGLE-Decoding). With this optimization, the decoding speed can be improved by **1.8x** for batch size 1 and **1.5x** for batch size 32 respectively on H200 TP8 setting.
 
 **Usage**:
 Add arguments `--speculative-algorithm`, `--speculative-num-steps`, `--speculative-eagle-topk` and `--speculative-num-draft-tokens` to enable this feature. For example:
@@ -176,7 +176,7 @@ python3 -m sglang.launch_server \
 
 ### Reasoning Content for DeepSeek R1 & V3.1
 
-See [Reasoning Parser](https://docs.sglang.ai/advanced_features/separate_reasoning.html) and [Thinking Parameter for DeepSeek V3.1](https://docs.sglang.ai/basic_usage/openai_api_completions.html#Example:-DeepSeek-V3-Models).
+See [Reasoning Parser](https://docs.sglang.io/advanced_features/separate_reasoning.html) and [Thinking Parameter for DeepSeek V3.1](https://docs.sglang.io/basic_usage/openai_api_completions.html#Example:-DeepSeek-V3-Models).
 
 
 ### Function calling for DeepSeek Models

docs/basic_usage/deepseek_v32.md

@@ -59,7 +59,7 @@ python -m sglang.launch_server --model deepseek-ai/DeepSeek-V3.2-Exp --tp 8
   - B200: `flashmla_auto` prefill attention (short-seq prefill uses MHA via TRT-LLM ragged), `flashmla_kv` decode attention, `fp8_e4m3` kv cache dtype. `flashmla_auto` enables automatic selection of either `flashmla_sparse` or `flashmla_kv` kernel for prefill based on KV cache dtype, hardware, and heuristics. When FP8 KV cache is enabled and `total_kv_tokens < total_q_tokens * 512`, it uses the `flashmla_sparse` kernel; otherwise, it falls back to the `flashmla_kv` kernel. The heuristics may need to be tuned if the performance of either the `flashmla_sparse` or `flashmla_kv` kernel changes significantly.
 
 ## Multi-token Prediction
-SGLang implements Multi-Token Prediction (MTP) for DeepSeek V3.2 based on [EAGLE speculative decoding](https://docs.sglang.ai/advanced_features/speculative_decoding.html#EAGLE-Decoding). With this optimization, the decoding speed can be improved significantly on small batch sizes. Please look at [this PR](https://github.com/sgl-project/sglang/pull/11652) for more information.
+SGLang implements Multi-Token Prediction (MTP) for DeepSeek V3.2 based on [EAGLE speculative decoding](https://docs.sglang.io/advanced_features/speculative_decoding.html#EAGLE-Decoding). With this optimization, the decoding speed can be improved significantly on small batch sizes. Please look at [this PR](https://github.com/sgl-project/sglang/pull/11652) for more information.
 
 Example usage:
 ```bash
@@ -70,7 +70,7 @@ python -m sglang.launch_server --model deepseek-ai/DeepSeek-V3.2-Exp --tp 8 --dp
 
 
 ## Function Calling and Reasoning Parser
-The usage of function calling and reasoning parser is the same as DeepSeek V3.1. Please refer to [Reasoning Parser](https://docs.sglang.ai/advanced_features/separate_reasoning.html) and [Tool Parser](https://docs.sglang.ai/advanced_features/tool_parser.html) documents.
+The usage of function calling and reasoning parser is the same as DeepSeek V3.1. Please refer to [Reasoning Parser](https://docs.sglang.io/advanced_features/separate_reasoning.html) and [Tool Parser](https://docs.sglang.io/advanced_features/tool_parser.html) documents.
 
 ## PD Disaggregation
 

docs/basic_usage/llama4.md

@@ -36,7 +36,7 @@ python3 -m sglang.launch_server \
 
 
 ### EAGLE Speculative Decoding
-**Description**: SGLang has supported Llama 4 Maverick (400B) with [EAGLE speculative decoding](https://docs.sglang.ai/advanced_features/speculative_decoding.html#EAGLE-Decoding).
+**Description**: SGLang has supported Llama 4 Maverick (400B) with [EAGLE speculative decoding](https://docs.sglang.io/advanced_features/speculative_decoding.html#EAGLE-Decoding).
 
 **Usage**:
 Add arguments `--speculative-draft-model-path`, `--speculative-algorithm`, `--speculative-num-steps`, `--speculative-eagle-topk` and `--speculative-num-draft-tokens` to enable this feature. For example:

docs/basic_usage/qwen3.md

@@ -15,7 +15,7 @@ python3 -m sglang.launch_server --model Qwen/Qwen3-Next-80B-A3B-Instruct --tp 4
 - `--mamba-ssm-dtype`: `bfloat16` or `float32`, use `bfloat16` to save mamba cache size and `float32` to get more accurate results. The default setting is `float32`.
 
 ### EAGLE Speculative Decoding
-**Description**: SGLang has supported Qwen3-Next models with [EAGLE speculative decoding](https://docs.sglang.ai/advanced_features/speculative_decoding.html#EAGLE-Decoding).
+**Description**: SGLang has supported Qwen3-Next models with [EAGLE speculative decoding](https://docs.sglang.io/advanced_features/speculative_decoding.html#EAGLE-Decoding).
 
 **Usage**:
 Add arguments `--speculative-algorithm`, `--speculative-num-steps`, `--speculative-eagle-topk` and `--speculative-num-draft-tokens` to enable this feature. For example:

docs/platforms/amd_gpu.md

@@ -100,7 +100,7 @@ The steps below show how to build and use an image.
        --port 30000
    ```
 
-4. To verify the utility, you can run a benchmark in another terminal or refer to [other docs](https://docs.sglang.ai/basic_usage/openai_api_completions.html) to send requests to the engine.
+4. To verify the utility, you can run a benchmark in another terminal or refer to [other docs](https://docs.sglang.io/basic_usage/openai_api_completions.html) to send requests to the engine.
 
    ```bash
    drun sglang_image \

docs/platforms/cpu_server.md

@@ -173,7 +173,7 @@ python -m sglang.bench_serving -h
 ```
 
 Additionally, the requests can be formed with
-[OpenAI Completions API](https://docs.sglang.ai/basic_usage/openai_api_completions.html)
+[OpenAI Completions API](https://docs.sglang.io/basic_usage/openai_api_completions.html)
 and sent via the command line (e.g. using `curl`) or via your own script.
 
 ## Example: Running DeepSeek-V3.1-Terminus

docs/platforms/nvidia_jetson.md

@@ -49,7 +49,7 @@ python -m sglang.launch_server \
 ```
 The quantization and limited context length (`--dtype half --context-length 8192`) are due to the limited computational resources in [Nvidia jetson kit](https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-orin/). A detailed explanation can be found in [Server Arguments](../advanced_features/server_arguments.md).
 
-After launching the engine, refer to [Chat completions](https://docs.sglang.ai/basic_usage/openai_api_completions.html#Usage) to test the usability.
+After launching the engine, refer to [Chat completions](https://docs.sglang.io/basic_usage/openai_api_completions.html#Usage) to test the usability.
 * * * * *
 Running quantization with TorchAO
 -------------------------------------

docs/platforms/xpu.md

@@ -88,5 +88,5 @@ python -m sglang.bench_serving -h
 ```
 
 Additionally, the requests can be formed with
-[OpenAI Completions API](https://docs.sglang.ai/basic_usage/openai_api_completions.html)
+[OpenAI Completions API](https://docs.sglang.io/basic_usage/openai_api_completions.html)
 and sent via the command line (e.g. using `curl`) or via your own script.

docs/references/frontend/frontend_tutorial.ipynb

@@ -385,7 +385,7 @@
     "## Multi-modal Generation\n",
     "\n",
     "You may use SGLang frontend language to define multi-modal prompts.\n",
-    "See [here](https://docs.sglang.ai/supported_models/generative_models.html) for supported models."
+    "See [here](https://docs.sglang.io/supported_models/generative_models.html) for supported models."
    ]
   },
   {

docs/references/learn_more.md

@@ -1,8 +1,9 @@
 # Learn More and Join the Community
 
-- The development roadmap: https://roadmap.sglang.io
+- The development roadmap: [https://roadmap.sglang.io](https://roadmap.sglang.io)
+- Join weekly public development meeting: [https://meet.sglang.io](https://meet.sglang.io)
+- Join Slack: [https://slack.sglang.io/](https://slack.sglang.io/)
+- Follow on X (formerly Twitter): [https://x.com/lmsysorg](https://x.com/lmsysorg)
+- Follow on LinkedIn: [https://www.linkedin.com/company/sgl-project/](https://www.linkedin.com/company/sgl-project/)
 - The latest SGLang features and updates are shared through the [LMSYS blog](https://lmsys.org/blog/)
-- X (formerly Twitter): https://x.com/lmsysorg
-- LinkedIn: https://www.linkedin.com/company/sgl-project/
-- Join Slack: https://slack.sglang.io/
 - More blogs, slides, and videos about SGLang at [https://github.com/sgl-project/sgl-learning-materials](https://github.com/sgl-project/sgl-learning-materials)

docs/references/multi_node_deployment/multi_node.md

@@ -30,7 +30,7 @@ python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3.1-405B-Instr
 
 ## DeepSeek V3/R1
 
-Please refer to [DeepSeek documents for reference](https://docs.sglang.ai/basic_usage/deepseek.html#running-examples-on-multi-node).
+Please refer to [DeepSeek documents for reference](https://docs.sglang.io/basic_usage/deepseek.html#running-examples-on-multi-node).
 
 ## Multi-Node Inference on SLURM
 
@@ -95,6 +95,6 @@ echo "[INFO] $HEAD_NODE:30000 is ready to accept connections"
 wait
 ```
 
-Then, you can test the server by sending requests following other [documents](https://docs.sglang.ai/basic_usage/openai_api_completions.html).
+Then, you can test the server by sending requests following other [documents](https://docs.sglang.io/basic_usage/openai_api_completions.html).
 
 Thanks for [aflah02](https://github.com/aflah02) for providing the example, based on his [blog post](https://aflah02.substack.com/p/multi-node-llm-inference-with-sglang).

docs/supported_models/multimodal_language_models.md

@@ -11,7 +11,7 @@ python3 -m sglang.launch_server \
   --port 30000 \
 ```
 
-> See the [OpenAI APIs section](https://docs.sglang.ai/basic_usage/openai_api_vision.html) for how to send multimodal requests.
+> See the [OpenAI APIs section](https://docs.sglang.io/basic_usage/openai_api_vision.html) for how to send multimodal requests.
 
 ## Supported models
 

docs/supported_models/support_new_models.md

@@ -79,7 +79,7 @@ ONLY_RUN=Qwen/Qwen2-1.5B python3 -m unittest test_generation_models.TestGenerati
 
 ### Benchmark
 
-- **(Required) MMMU**: follow MMMU benchmark [README.md](https://github.com/sgl-project/sglang/blob/main/benchmark/mmmu/README.md) to get SGLang vs. HF Transformer accuracy comparison. The accuracy score from SGLang run should not be much lower than that from HF Transformer run. Similarly, follow https://docs.sglang.ai/developer_guide/benchmark_and_profiling.html to get performance comparison: TTFT and throughput must meet or exceed baselines (e.g., HF Transformer).
+- **(Required) MMMU**: follow MMMU benchmark [README.md](https://github.com/sgl-project/sglang/blob/main/benchmark/mmmu/README.md) to get SGLang vs. HF Transformer accuracy comparison. The accuracy score from SGLang run should not be much lower than that from HF Transformer run. Similarly, follow https://docs.sglang.io/developer_guide/benchmark_and_profiling.html to get performance comparison: TTFT and throughput must meet or exceed baselines (e.g., HF Transformer).
 - **(Optional) Other evals**: If you ran other evals, please note the results in PR description.
 
 ## Port a Model from vLLM to SGLang