[None][doc] Fix a invalid link and a typo.

Signed-off-by: nv-guomingz <[email protected]>

Author: nv-guomingz

docs/source/developer-guide/perf-benchmarking.md

@@ -8,7 +8,7 @@ Expect breaking API changes.
 ```
 
 TensorRT LLM provides the `trtllm-bench` CLI, a packaged benchmarking utility that aims to make it
-easier for users to reproduce our officially published [performance overiew](./perf-overview.md#throughput-measurements). `trtllm-bench` provides the follows:
+easier for users to reproduce our officially published [performance overview](./perf-overview.md#throughput-measurements). `trtllm-bench` provides the follows:
 
 - A streamlined way to build tuned engines for benchmarking for a variety of models and platforms.
 - An entirely Python workflow for benchmarking.

docs/source/overview.md

@@ -18,7 +18,7 @@ TensorRT LLM is designed to be modular and easy to modify. Its PyTorch-native ar
 
 TensorRT LLM delivers breakthrough performance on the latest NVIDIA GPUs:
 
-- **DeepSeek R1**: [World-record inference performance on Blackwell GPUs](.https://developer.nvidia.com/blog/nvidia-blackwell-delivers-world-record-deepseek-r1-inference-performance/)
+- **DeepSeek R1**: [World-record inference performance on Blackwell GPUs](https://developer.nvidia.com/blog/nvidia-blackwell-delivers-world-record-deepseek-r1-inference-performance/)
 - **Llama 4 Maverick**: [Breaks the 1,000 TPS/User Barrier on B200 GPUs](https://developer.nvidia.com/blog/blackwell-breaks-the-1000-tps-user-barrier-with-metas-llama-4-maverick/)
 
 ### 🎯 **Comprehensive Model Support**