[Docs] fix dead links in multiple documentation pages (#12764)

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/backend/hyperparameter_tuning.html#enabling-cache-for-torch-compile).
+- 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 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.md

@@ -90,7 +90,7 @@ Please refer to [the example](https://github.com/sgl-project/sglang/tree/main/be
 
 - **Weight Absorption**: By applying the associative law of matrix multiplication to reorder computation steps, this method balances computation and memory access and improves efficiency in the decoding phase.
 
-- **MLA Attention Backends**: Currently SGLang supports different optimized MLA attention backends, including [FlashAttention3](https://github.com/Dao-AILab/flash-attention), [Flashinfer](https://docs.flashinfer.ai/api/mla.html), [FlashMLA](https://github.com/deepseek-ai/FlashMLA), [CutlassMLA](https://github.com/sgl-project/sglang/pull/5390), **TRTLLM MLA** (optimized for Blackwell architecture), and [Triton](https://github.com/triton-lang/triton) backends. The default FA3 provides good performance across wide workloads.
+- **MLA Attention Backends**: Currently SGLang supports different optimized MLA attention backends, including [FlashAttention3](https://github.com/Dao-AILab/flash-attention), [Flashinfer](https://docs.flashinfer.ai/api/attention.html#flashinfer-mla), [FlashMLA](https://github.com/deepseek-ai/FlashMLA), [CutlassMLA](https://github.com/sgl-project/sglang/pull/5390), **TRTLLM MLA** (optimized for Blackwell architecture), and [Triton](https://github.com/triton-lang/triton) backends. The default FA3 provides good performance across wide workloads.
 
 - **FP8 Quantization**: W8A8 FP8 and KV Cache FP8 quantization enables efficient FP8 inference. Additionally, we have implemented Batched Matrix Multiplication (BMM) operator to facilitate FP8 inference in MLA with weight absorption.
 

docs/basic_usage/llama4.md

@@ -27,7 +27,7 @@ python3 -m sglang.launch_server \
 
 
 ### EAGLE Speculative Decoding
-**Description**: SGLang has supported Llama 4 Maverick (400B) with [EAGLE speculative decoding](https://docs.sglang.ai/backend/speculative_decoding.html#EAGLE-Decoding).
+**Description**: SGLang has supported Llama 4 Maverick (400B) with [EAGLE speculative decoding](https://docs.sglang.ai/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/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/backend/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.ai/basic_usage/openai_api_completions.html) to send requests to the engine.
 
    ```bash
    drun sglang_image \

docs/platforms/nvidia_jetson.md

@@ -47,9 +47,9 @@ python -m sglang.launch_server \
   --mem-fraction-static 0.8 \
   --context-length 8192
 ```
-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](../backend/server_arguments.md).
+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/backend/openai_api_completions.html#Usage) to test the usability.
+After launching the engine, refer to [Chat completions](https://docs.sglang.ai/basic_usage/openai_api_completions.html#Usage) to test the usability.
 * * * * *
 Running quantization with TorchAO
 -------------------------------------

docs/references/multi_node_deployment/multi_node.md

@@ -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/backend/openai_api_completions.html).
+Then, you can test the server by sending requests following other [documents](https://docs.sglang.ai/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).

examples/runtime/README.md

@@ -16,12 +16,12 @@ The below examples will mostly need you to start a server in a separate terminal
 
 ## Engine
 
-The `engine` folder contains that examples that show how to use [Offline Engine API](https://docs.sglang.ai/backend/offline_engine_api.html#Offline-Engine-API) for common workflows.
+The `engine` folder contains that examples that show how to use [Offline Engine API](https://docs.sglang.ai/basic_usage/offline_engine_api.html#Offline-Engine-API) for common workflows.
 
 * `custom_server.py`: An example how to deploy a custom server.
 * `embedding.py`: An example how to extract embeddings.
 * `launch_engine.py`: An example how to launch the Engine.
-* `offline_batch_inference_eagle.py`: An example how to perform speculative decoding using [EAGLE](https://docs.sglang.ai/backend/speculative_decoding.html).
+* `offline_batch_inference_eagle.py`: An example how to perform speculative decoding using [EAGLE](https://docs.sglang.ai/advanced_features/speculative_decoding.html).
 * `offline_batch_inference_torchrun.py`: An example how to perform inference using [torchrun](https://pytorch.org/docs/stable/elastic/run.html).
 * `offline_batch_inference_vlm.py`: An example how to use VLMs with the engine.
 * `offline_batch_inference.py`: An example how to use the engine to perform inference on a batch of examples.