[Doc] Fix a 404 link in installation/cpu.md (vllm-project#16773)

Signed-off-by: windsonsea <haifeng.yao@daocloud.io>
Signed-off-by: Mu Huai <tianbowen.tbw@antgroup.com>

Author: windsonsea

docs/source/getting_started/installation/cpu.md

@@ -272,7 +272,7 @@ $ python examples/offline_inference/basic/basic.py
 
 - Decouple the HTTP serving components from the inference components. In a GPU backend configuration, the HTTP serving and tokenization tasks operate on the CPU, while inference runs on the GPU, which typically does not pose a problem. However, in a CPU-based setup, the HTTP serving and tokenization can cause significant context switching and reduced cache efficiency. Therefore, it is strongly recommended to segregate these two components for improved performance.
 
-- On CPU based setup with NUMA enabled, the memory access performance may be largely impacted by the [topology](https://github.com/intel/intel-extension-for-pytorch/blob/main/docs/tutorials/performance_tuning/tuning_guide.inc.md#non-uniform-memory-access-numa). For NUMA architecture, Tensor Parallel is a option for better performance.
+- On CPU based setup with NUMA enabled, the memory access performance may be largely impacted by the [topology](https://github.com/intel/intel-extension-for-pytorch/blob/main/docs/tutorials/performance_tuning/tuning_guide.md#non-uniform-memory-access-numa). For NUMA architecture, Tensor Parallel is a option for better performance.
 
   - Tensor Parallel is supported for serving and offline inferencing. In general each NUMA node is treated as one GPU card. Below is the example script to enable Tensor Parallel = 2 for serving: