diff --git a/vllm/config/kernel.py b/vllm/config/kernel.py
index cd1408c3e7fa..e9f41c538ad9 100644
--- a/vllm/config/kernel.py
+++ b/vllm/config/kernel.py
@@ -199,7 +199,7 @@ class KernelConfig:
     - "auto": Automatically select the best backend based on model and hardware
     - "cutlass": Use CUTLASS-based kernels
     - "flashinfer_cutlass": Use FlashInfer with CUTLASS kernels
-    - "flashinfer_cutedsl": Use FlashInfer with CuteDSL kernels
+    - "flashinfer_cutedsl": Use FlashInfer with CuTe-DSL kernels (NVFP4, MXFP8)
     - "flashinfer_trtllm": Use FlashInfer with TensorRT-LLM kernels
     - "flashinfer_cudnn": Use FlashInfer with cuDNN kernels
     - "flashinfer_b12x": Use FlashInfer b12x CuteDSL NVFP4 GEMM (SM120+)
diff --git a/vllm/model_executor/kernels/linear/__init__.py b/vllm/model_executor/kernels/linear/__init__.py
index 58ba7c8cb403..4ac8d49cd58e 100644
--- a/vllm/model_executor/kernels/linear/__init__.py
+++ b/vllm/model_executor/kernels/linear/__init__.py
@@ -88,6 +88,7 @@
     EmulationMxfp8LinearKernel,
 )
 from vllm.model_executor.kernels.linear.mxfp8.flashinfer import (
+    FlashInferCutedslMxfp8LinearKernel,
     FlashInferCutlassMxfp8LinearKernel,
 )
 from vllm.model_executor.kernels.linear.mxfp8.marlin import (
@@ -212,6 +213,7 @@ def _get_linear_backend() -> str:
     },
     "flashinfer_cutedsl": {
         FlashInferCuteDslNvFp4LinearKernel,
+        FlashInferCutedslMxfp8LinearKernel,
     },
     "flashinfer_trtllm": {
         FlashInferTrtllmNvFp4LinearKernel,
@@ -385,6 +387,7 @@ def _filter_kernels_by_backend(
 # in priority/performance order (when available)
 _POSSIBLE_MXFP8_KERNELS: dict[PlatformEnum, list[type[Mxfp8LinearKernel]]] = {
     PlatformEnum.CUDA: [
+        FlashInferCutedslMxfp8LinearKernel,
         FlashInferCutlassMxfp8LinearKernel,
         MarlinMxfp8LinearKernel,
         EmulationMxfp8LinearKernel,
@@ -1036,6 +1039,7 @@ def register_linear_kernel(
     "MxFp4LinearLayerConfig",
     "FlashInferMxFp4LinearKernel",
     "MarlinMxFp4LinearKernel",
+    "FlashInferCutedslMxfp8LinearKernel",
     "FlashInferCutlassMxfp8LinearKernel",
     "MarlinMxfp8LinearKernel",
     "XPUMxFp8LinearKernel",
diff --git a/vllm/model_executor/kernels/linear/mxfp8/flashinfer.py b/vllm/model_executor/kernels/linear/mxfp8/flashinfer.py
index 8188fd596096..d26e5579edbb 100644
--- a/vllm/model_executor/kernels/linear/mxfp8/flashinfer.py
+++ b/vllm/model_executor/kernels/linear/mxfp8/flashinfer.py
@@ -11,6 +11,7 @@
 )
 from vllm.platforms import current_platform
 from vllm.utils import flashinfer as vllm_flashinfer
+from vllm.utils.flashinfer import has_flashinfer_cutedsl
 
 from .Mxfp8LinearKernel import Mxfp8LinearKernel, Mxfp8LinearLayerConfig
 
@@ -91,3 +92,84 @@ def apply_weights(
 
         output_shape = (*input_shape[:-1], N)
         return output.view(output_shape)
+
+
+class FlashInferCutedslMxfp8LinearKernel(Mxfp8LinearKernel):
+    """MXFP8 W8A8 GEMM via FlashInfer CuTe-DSL (SM100/SM103)."""
+
+    @classmethod
+    def is_supported(
+        cls, compute_capability: int | None = None
+    ) -> tuple[bool, str | None]:
+        if not (
+            current_platform.is_cuda()
+            and current_platform.is_device_capability_family(100)
+        ):
+            return False, "requires sm_100/sm_103 (Blackwell)"
+        if not has_flashinfer_cutedsl():
+            return False, "requires FlashInfer CuTe-DSL module"
+        return True, None
+
+    @classmethod
+    def can_implement(cls, c: Mxfp8LinearLayerConfig) -> tuple[bool, str | None]:
+        return True, None
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        weight = layer.weight.data  # [N, K]
+        N, K = weight.shape
+
+        scale_k = K // MXFP8_BLOCK_SIZE
+        weight_scale_2d = layer.weight_scale.data[:N, :scale_k].contiguous()
+        weight_scale_swizzled = swizzle_mxfp8_scale(weight_scale_2d, M=N, K=K)
+
+        # Store weight column-major [K, N] as mm_mxfp8 expects for operand B.
+        layer.weight = Parameter(weight.contiguous().t(), requires_grad=False)
+        layer.weight_scale = Parameter(
+            weight_scale_swizzled.contiguous(), requires_grad=False
+        )
+
+    def apply_weights(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        bias: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        weight = layer.weight  # [K, N], column-major
+        weight_scale = layer.weight_scale
+        out_dtype = x.dtype
+        K, N = weight.shape
+
+        input_shape = x.shape
+        input_2d = x.view(-1, K)
+        min_dim = 128
+
+        assert min_dim <= K, (
+            f"mm_mxfp8 requires K >= {min_dim}, got K={K}. "
+            f"in_features is too small for mm_mxfp8."
+        )
+        assert K % MXFP8_BLOCK_SIZE == 0, (
+            f"mm_mxfp8 requires K to be divisible by {MXFP8_BLOCK_SIZE}, got K={K}."
+        )
+        assert min_dim <= N, (
+            f"mm_mxfp8 requires N >= {min_dim}, got N={N}. "
+            f"out_features is too small for mm_mxfp8."
+        )
+
+        input_mxfp8, input_scale = mxfp8_e4m3_quantize(
+            input_2d, is_sf_swizzled_layout=True
+        )
+
+        output = vllm_flashinfer.mm_mxfp8(
+            input_mxfp8,
+            weight,
+            input_scale,
+            weight_scale,
+            out_dtype=out_dtype,
+            backend="cute-dsl",
+        )
+
+        if bias is not None:
+            output = output + bias
+
+        output_shape = (*input_shape[:-1], N)
+        return output.view(output_shape)