diff --git a/csrc/quantization/activation_kernels.cu b/csrc/quantization/activation_kernels.cu
index 6fcd246f63c5..2521b2797e2c 100644
--- a/csrc/quantization/activation_kernels.cu
+++ b/csrc/quantization/activation_kernels.cu
@@ -578,11 +578,13 @@ void persistent_masked_m_silu_mul_quant(
 
   // This kernel currently only supports H % 128 == 0 and assumes a
   // fixed GROUP_SIZE of 128.
+  static constexpr int GROUP_SIZE = 128;
+
   TORCH_CHECK(input.dtype() == torch::kBFloat16);
   TORCH_CHECK(y_q.dtype() == torch::kFloat8_e4m3fn ||
               y_q.dtype() == torch::kFloat8_e4m3fnuz);
   TORCH_CHECK(y_s.dtype() == torch::kFloat32);
-  TORCH_CHECK(input.size(-1) % 256 == 0);
+  TORCH_CHECK(input.size(-1) % (GROUP_SIZE * 2) == 0);
 
   using Idx_t = int64_t;
 
@@ -601,8 +603,6 @@ void persistent_masked_m_silu_mul_quant(
 
   Idx_t stride_counts_e = tokens_per_expert.stride(0);
 
-  static constexpr int GROUP_SIZE = 128;
-
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
 
   #define KERNEL(BLOCK_COUNT, USE_UE8M0, THREAD_COUNT, STAGES)                 \
@@ -628,21 +628,26 @@ void persistent_masked_m_silu_mul_quant(
 
   static constexpr int SILU_V2_BLOCK_COUNT = 132 * 32;
 
+  int const NUM_GROUPS = H / GROUP_SIZE;
   if (!use_ue8m0) {
-    if (H >= 4096) {
+    if (H >= 4096 && (NUM_GROUPS % 8 == 0)) {
+      /* 8 warps config */
       static constexpr int NUM_STAGES = 4;
       static constexpr int THREAD_COUNT = 256;
       KERNEL(SILU_V2_BLOCK_COUNT, false, THREAD_COUNT, NUM_STAGES);
     } else {
+      /* 1 warp config */
       static constexpr int THREAD_COUNT = 32;
       KERNEL(SILU_V2_BLOCK_COUNT, false, THREAD_COUNT, 2);
     }
   } else {
-    if (H >= 4096) {
+    if (H >= 4096 && (NUM_GROUPS % 8 == 0)) {
+      /* 8 warps config */
       static constexpr int NUM_STAGES = 4;
       static constexpr int THREAD_COUNT = 256;
       KERNEL(SILU_V2_BLOCK_COUNT, true, THREAD_COUNT, NUM_STAGES);
     } else {
+      /* 1 warp config */
       static constexpr int THREAD_COUNT = 32;
       KERNEL(SILU_V2_BLOCK_COUNT, true, THREAD_COUNT, 2);
     }
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
index 97a55c37b9a3..420dbbffaac0 100644
--- a/tests/kernels/moe/test_silu_mul_fp8_quant_deep_gemm.py
+++ b/tests/kernels/moe/test_silu_mul_fp8_quant_deep_gemm.py
@@ -25,6 +25,7 @@
     (8, 16, 128 * 2, fp8_dtype),
     (8, 16, 128 * 3, fp8_dtype),
     (8, 64, 7168, fp8_dtype),
+    (8, 128, 128 * 33, fp8_dtype),
     (8, 128, 7168, fp8_dtype),
     (8, 512, 7168, fp8_dtype),
     (8, 1024, 7168, fp8_dtype),
@@ -54,8 +55,10 @@ def test_silu_mul_fp8_quant_deep_gemm(E, T, H, fp8_type):
     )
 
     # Run the SiLU V2 kernel
+    # TODO (varun): use_e8m0 is set to false as the reference impl does
+    # not handle that case.
     y_q, y_s = persistent_masked_m_silu_mul_quant(
-        y, tokens_per_expert, group_size=group_size
+        y, tokens_per_expert, group_size=group_size, use_ue8m0=False
     )
 
     torch.cuda.synchronize()
diff --git a/vllm/model_executor/layers/fused_moe/batched_deep_gemm_moe.py b/vllm/model_executor/layers/fused_moe/batched_deep_gemm_moe.py
index 095ec966ea7e..b8a97e92ab79 100644
--- a/vllm/model_executor/layers/fused_moe/batched_deep_gemm_moe.py
+++ b/vllm/model_executor/layers/fused_moe/batched_deep_gemm_moe.py
@@ -100,6 +100,7 @@ def persistent_masked_m_silu_mul_quant(
     tokens_per_expert: torch.Tensor,  # (E,) number of valid tokens per expert
     num_parallel_tokens=16,
     group_size: int = 128,
+    use_ue8m0: bool | None = None,
 ) -> tuple[torch.Tensor, torch.Tensor]:
     """Quantize silu(y[..., :H]) * y[..., H:] to FP8 with group per-token scales
     y has shape (E, T, 2*H). The first half of the last dimension is
@@ -164,7 +165,7 @@ def persistent_masked_m_silu_mul_quant(
         device=y.device,
     )
 
-    use_ue8m0 = is_deep_gemm_e8m0_used()
+    use_ue8m0 = use_ue8m0 if use_ue8m0 is not None else is_deep_gemm_e8m0_used()
 
     cuda_arch = current_platform.get_device_capability(
         device_id=y.device.index