diff --git a/tests/kernels/moe/test_silu_mul_fp8_quant_deep_gemm.py b/tests/kernels/moe/test_silu_mul_fp8_quant_deep_gemm.py
new file mode 100644
index 0000000000000..5cfb4266ff933
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+++ b/tests/kernels/moe/test_silu_mul_fp8_quant_deep_gemm.py
@@ -0,0 +1,82 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+import torch
+
+from vllm.model_executor.layers.fused_moe.batched_deep_gemm_moe import (
+    silu_mul_fp8_quant_deep_gemm)
+from vllm.platforms import current_platform
+
+# (E, T, H, group_size, seed)
+CASES = [
+    (1, 1, 128, 64, 0),
+    (1, 4, 128, 128, 0),
+    (2, 4, 256, 128, 0),
+    (32, 64, 256, 128, 0),
+    (17, 31, 768, 128, 0),
+]
+
+
+@pytest.mark.parametrize("E,T,H,group_size,seed", CASES)
+@torch.inference_mode()
+def test_silu_mul_fp8_quant_deep_gemm(E, T, H, group_size, seed):
+    current_platform.seed_everything(seed)
+
+    # Input tensor of shape (E, T, 2*H)
+    y = torch.randn((E, T, 2 * H), dtype=torch.float32, device="cuda")
+    tokens_per_expert = torch.randint(
+        low=0,
+        high=T,
+        size=(E, ),
+        dtype=torch.int32,
+        device="cuda",
+    )
+
+    # Run the Triton kernel
+    y_q, y_s = silu_mul_fp8_quant_deep_gemm(y,
+                                            tokens_per_expert,
+                                            group_size=group_size,
+                                            eps=1e-10)
+
+    # Reference implementation
+    fp8_info = torch.finfo(torch.float8_e4m3fn)
+    fp8_max = fp8_info.max
+    fp8_min = fp8_info.min
+    eps = 1e-10
+
+    # Compute silu activation and elementwise multiplication
+    y1 = y[..., :H]
+    y2 = y[..., H:]
+    silu_x = y1 * torch.sigmoid(y1)
+    merged = silu_x * y2
+
+    # Compute reference scales and quantized output
+    ref_s = torch.empty((E, T, H // group_size),
+                        dtype=torch.float32,
+                        device="cuda")
+    ref_q = torch.empty((E, T, H), dtype=torch.float8_e4m3fn, device="cuda")
+    # Compute reference scales and quantized output, skipping padded tokens
+    for e in range(E):
+        nt = tokens_per_expert[e].item()
+        for t in range(nt):
+            data = merged[e, t]
+            data_grp = data.view(H // group_size, group_size)
+            amax = data_grp.abs().amax(dim=1).clamp(min=eps)
+            scale = amax / fp8_max
+
+            scaled = data / scale.repeat_interleave(group_size)
+            clamped = scaled.clamp(fp8_min, fp8_max)
+            q = clamped.to(torch.float8_e4m3fn)
+
+            ref_s[e, t] = scale
+            ref_q[e, t] = q
+
+    # Compare scales and quantized outputs for valid tokens only
+    for e in range(E):
+        nt = tokens_per_expert[e].item()
+        torch.testing.assert_close(y_s[e, :nt], ref_s[e, :nt])
+        torch.testing.assert_close(
+            y_q[e, :nt].to(torch.float32),
+            ref_q[e, :nt].to(torch.float32),
+        )