diff --git a/.buildkite/intel_jobs/test-intel.yaml b/.buildkite/intel_jobs/test-intel.yaml
index a7e6fa87f5..c278b10bc4 100644
--- a/.buildkite/intel_jobs/test-intel.yaml
+++ b/.buildkite/intel_jobs/test-intel.yaml
@@ -70,8 +70,10 @@ steps:
       - >-
         bash .buildkite/scripts/hardware_ci/run-intel-test.sh
         'python3 examples/basic/offline_inference/generate.py --linear-backend xpu --model RedHatAI/Meta-Llama-3.1-8B-Instruct-FP8 --enforce-eager --max-model-len 4096 &&
+        python3 examples/basic/offline_inference/generate.py --linear-backend torch --model RedHatAI/Meta-Llama-3.1-8B-Instruct-FP8 --enforce-eager --max-model-len 4096 &&
         python3 examples/basic/offline_inference/generate.py --linear-backend xpu --model neuralmagic/Llama-3.2-1B-Instruct-FP8-dynamic --enforce-eager --max-model-len 4096 &&
-        python3 examples/basic/offline_inference/generate.py --linear-backend xpu --model meta-llama/Llama-3.2-1B-Instruct --quantization fp8 --enforce-eager --max-model-len 4096
+        python3 examples/basic/offline_inference/generate.py --linear-backend xpu --model meta-llama/Llama-3.2-1B-Instruct --quantization fp8 --enforce-eager --max-model-len 4096 &&
+        python3 examples/basic/offline_inference/generate.py --linear-backend torch --model meta-llama/Llama-3.2-1B-Instruct --quantization fp8 --enforce-eager --max-model-len 4096
         '
   - label: "XPU V1 test"
     depends_on:
diff --git a/docs/features/quantization/online.md b/docs/features/quantization/online.md
index a4da52557a..67e28a270d 100644
--- a/docs/features/quantization/online.md
+++ b/docs/features/quantization/online.md
@@ -62,7 +62,7 @@ weight name. Unset fields fall back to the `--quantization` shorthand's
 defaults, or for already-quantized checkpoints to whatever the checkpoint
 declares.
 
-On XPU, non-block FP8 scaled-mm linear layers default to W8A16; setting `--linear-backend xpu` forces W8A8. Use `--linear-backend xpu_woq` to explicitly select weight-only quantization (W8A16).
+On XPU, non-block FP8 scaled-mm linear layers default to W8A16; setting `--linear-backend xpu` forces W8A8. Use `--linear-backend xpu_woq` to explicitly select weight-only quantization (W8A16). Setting `--linear-backend torch` also forces W8A8 but runs the GEMM through `torch._scaled_mm` instead of the custom XPU kernel.
 
 The CLI accepts the same shape as JSON or as dotted keys:
 
diff --git a/vllm/model_executor/kernels/linear/__init__.py b/vllm/model_executor/kernels/linear/__init__.py
index aaf46875e8..3d8449a219 100644
--- a/vllm/model_executor/kernels/linear/__init__.py
+++ b/vllm/model_executor/kernels/linear/__init__.py
@@ -363,6 +363,8 @@ _POSSIBLE_FP8_KERNELS: dict[PlatformEnum, list[type[FP8ScaledMMLinearKernel]]] =
     PlatformEnum.XPU: [
         XPUW8A16FP8LinearKernel,
         XPUW8A8FP8LinearKernel,
+        PerTensorTorchFP8ScaledMMLinearKernel,
+        ChannelWiseTorchFP8ScaledMMLinearKernel,
     ],
 }
 
diff --git a/vllm/model_executor/kernels/linear/scaled_mm/pytorch.py b/vllm/model_executor/kernels/linear/scaled_mm/pytorch.py
index a04be2e097..943dd2bb03 100644
--- a/vllm/model_executor/kernels/linear/scaled_mm/pytorch.py
+++ b/vllm/model_executor/kernels/linear/scaled_mm/pytorch.py
@@ -32,8 +32,12 @@ class TorchFP8ScaledMMLinearKernel(FP8ScaledMMLinearKernel):
     def is_supported(
         cls, compute_capability: int | None = None
     ) -> tuple[bool, str | None]:
-        if not (current_platform.is_cuda_alike() or current_platform.is_cpu()):
-            return False, "requires ROCm, CUDA or CPU."
+        if not (
+            current_platform.is_cuda_alike()
+            or current_platform.is_cpu()
+            or current_platform.is_xpu()
+        ):
+            return False, "requires ROCm, CUDA, CPU or XPU."
 
         if compute_capability is not None and compute_capability < 89:
             return False, "requires compute capability 89 and above."
diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py
index 6fea370824..473cad8741 100644
--- a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py
+++ b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py
@@ -811,12 +811,11 @@ class CompressedTensorsConfig(QuantizationConfig):
         if act_quant_format:
             if self._is_fp8_w8a8(weight_quant, input_quant):
                 if current_platform.is_xpu():
-                    # On XPU, --linear-backend xpu opts into W8A8 FP8
+                    # On XPU, --linear-backend xpu or torch opts into W8A8 FP8
                     # linear kernel; otherwise default to W8A16.
                     config = get_current_vllm_config_or_none()
-                    is_fp8_w8a8_supported = (
-                        config is not None
-                        and config.kernel_config.linear_backend == "xpu"
+                    is_fp8_w8a8_supported = config is not None and (
+                        config.kernel_config.linear_backend in ("xpu", "torch")
                     )
                 else:
                     is_fp8_w8a8_supported = self._check_scheme_supported(