diff --git a/docs/source/installation/linux.md b/docs/source/installation/linux.md
index 9bccba451c7..ab471e8c1db 100644
--- a/docs/source/installation/linux.md
+++ b/docs/source/installation/linux.md
@@ -9,14 +9,22 @@
    Before the pre-built Python wheel can be installed via `pip`, a few
    prerequisites must be put into place:
 
+   Install CUDA Toolkit following the [CUDA Installation Guide for Linux](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/) and
+   make sure `CUDA_HOME` environment variable is properly set.
+
    ```bash
-   # Optional step: Only required for Blackwell and Grace Hopper
+   # Optional step: Only required for NVIDIA Blackwell GPUs and SBSA platform
    pip3 install torch==2.7.1 torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128
 
+   # Optional step: Workaround for deep_gemm installation failure on SBSA platform
+   # The actual deep_gemm package and version should be obtained from the requirements.txt file.
+   pip3 install 'deep_gemm @ git+https://github.com/zongfeijing/DeepGEMM.git@a9d538ef4dff0326fe521c6ca0bfde115703b56a' \
+       --extra-index-url https://download.pytorch.org/whl/cu128
+
    sudo apt-get -y install libopenmpi-dev
    ```
 
-   PyTorch CUDA 12.8 package is required for supporting NVIDIA Blackwell and Grace Hopper GPUs. On prior GPUs, this extra installation is not required.
+   PyTorch CUDA 12.8 package is required for supporting NVIDIA Blackwell GPUs and SBSA platform. On prior GPUs or Linux x86_64 platform, this extra installation is not required.
 
    ```{tip}
    Instead of manually installing the preqrequisites as described
@@ -55,16 +63,3 @@ There are some known limitations when you pip install pre-built TensorRT-LLM whe
     when OMPI was not configured --with-slurm and we weren't able
     to discover a SLURM installation in the usual places.
     ```
-
-2. CUDA Toolkit
-
-    `pip install tensorrt-llm` won't install CUDA toolkit in your system, and the CUDA Toolkit is not required if want to just deploy a TensorRT-LLM engine.
-    TensorRT-LLM uses the [ModelOpt](https://nvidia.github.io/TensorRT-Model-Optimizer/) to quantize a model, while the ModelOpt requires CUDA toolkit to jit compile certain kernels which is not included in the pytorch to do quantization effectively.
-    Please install CUDA toolkit when you see the following message when running ModelOpt quantization.
-
-    ```
-    /usr/local/lib/python3.10/dist-packages/modelopt/torch/utils/cpp_extension.py:65:
-    UserWarning: CUDA_HOME environment variable is not set. Please set it to your CUDA install root.
-    Unable to load extension modelopt_cuda_ext and falling back to CPU version.
-    ```
-    The installation of CUDA toolkit can be found in [CUDA Toolkit Documentation](https://docs.nvidia.com/cuda/).
diff --git a/jenkins/L0_Test.groovy b/jenkins/L0_Test.groovy
index 4e795daa1a9..4c68e4796c8 100644
--- a/jenkins/L0_Test.groovy
+++ b/jenkins/L0_Test.groovy
@@ -2091,19 +2091,31 @@ def launchTestJobs(pipeline, testFilter, dockerNode=null)
                         trtllm_utils.llmExecStepWithRetry(pipeline, script: "pip3 uninstall -y tensorrt")
                         if (values[5] != DLFW_IMAGE) {
                             def ubuntu_version = key.contains("UB2404") ? "ubuntu2404" : "ubuntu2204"
-                            def platform = values[2] == X86_64_TRIPLE ? "x86_64" : "sbsa"
+                            def platform = cpu_arch == X86_64_TRIPLE ? "x86_64" : "sbsa"
                             trtllm_utils.llmExecStepWithRetry(pipeline, script: "wget https://developer.download.nvidia.com/compute/cuda/repos/${ubuntu_version}/${platform}/cuda-keyring_1.1-1_all.deb")
                             trtllm_utils.llmExecStepWithRetry(pipeline, script: "dpkg -i cuda-keyring_1.1-1_all.deb")
                             trtllm_utils.llmExecStepWithRetry(pipeline, script: "apt-get update")
                             trtllm_utils.llmExecStepWithRetry(pipeline, script: "apt-get -y install cuda-toolkit-12-9")
                         }
 
-                        // Extra PyTorch CUDA 12.8 install
+                        // Extra PyTorch CUDA 12.8 install for SBSA platform and Blackwell GPUs bare-metal environments
                         if (values[6]) {
                             echo "###### Extra PyTorch CUDA 12.8 install Start ######"
                             trtllm_utils.llmExecStepWithRetry(pipeline, script: "pip3 install torch==2.7.1 torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128")
                         }
 
+                        // Workaround for https://nvbugs/5433581 where deep_gemm installation fails on SBSA platform
+                        if (cpu_arch == AARCH64_TRIPLE) {
+                              echo "###### Workaround for https://nvbugs/5433581 Start ######"
+                              def deepGemmLine = readFile("${LLM_ROOT}/requirements.txt").readLines().find { it.trim().startsWith('deep_gemm') }
+                              if (deepGemmLine) {
+                                  trtllm_utils.llmExecStepWithRetry(pipeline, script: "pip3 install '${deepGemmLine.trim()}' --extra-index-url https://download.pytorch.org/whl/cu128")
+                              }
+                              else {
+                                echo "deep_gemm package not found in requirements.txt"
+                              }
+                        }
+
                         def libEnv = []
                         if (env.alternativeTRT) {
                             stage("Replace TensorRT") {