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 70ac6688deb7f..70a580b9c4c70 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
@@ -11,7 +11,8 @@ from vllm.model_executor.layers.fused_moe.topk_weight_and_reduce import (
     TopKWeightAndReduceDelegate)
 from vllm.model_executor.layers.fused_moe.utils import _resize_cache
 from vllm.triton_utils import tl, triton
-from vllm.utils.deep_gemm import fp8_m_grouped_gemm_nt_masked
+from vllm.utils.deep_gemm import (fp8_m_grouped_gemm_nt_masked,
+                                  is_blackwell_deep_gemm_used)
 
 logger = init_logger(__name__)
 
@@ -50,6 +51,7 @@ def _silu_mul_fp8_quant_deep_gemm(
     eps: tl.constexpr,
     fp8_min: tl.constexpr,
     fp8_max: tl.constexpr,
+    use_ue8m0: tl.constexpr,
 
     # Meta ---------------------------------------------------------------
     BLOCK: tl.constexpr,
@@ -92,7 +94,9 @@ def _silu_mul_fp8_quant_deep_gemm(
         y = x * y2
 
         _absmax = tl.maximum(tl.max(tl.abs(y)), eps)
-        y_s = _absmax / fp8_max
+        scale_raw = _absmax / fp8_max
+        y_s = tl.math.exp2(tl.ceil(
+            tl.log2(scale_raw))) if use_ue8m0 else scale_raw
         y_q = tl.clamp(y / y_s, fp8_min, fp8_max).to(y_q_ptr.dtype.element_ty)
 
         tl.store(y_q_ptr + base_yq_offset + cols * stride_yq_h, y_q, mask=mask)
@@ -174,6 +178,7 @@ def silu_mul_fp8_quant_deep_gemm(
         eps,
         fp8_min,
         fp8_max,
+        is_blackwell_deep_gemm_used(),
         BLOCK=group_size,
         num_warps=4,
     )
@@ -290,14 +295,10 @@ class BatchedDeepGemmExperts(mk.FusedMoEPermuteExpertsUnpermute):
         # may lead to better performance.
         expected_m = max_num_tokens
         fp8_m_grouped_gemm_nt_masked((a1q, a1q_scale), (w1, w1_scale),
-                                     out=workspace1,
-                                     masked_m=expert_num_tokens,
-                                     expected_m=expected_m)
+                                     workspace1, expert_num_tokens, expected_m)
 
         a2q, a2q_scale = silu_mul_fp8_quant_deep_gemm(workspace1,
                                                       expert_num_tokens)
 
-        fp8_m_grouped_gemm_nt_masked((a2q, a2q_scale), (w2, w2_scale),
-                                     out=output,
-                                     masked_m=expert_num_tokens,
-                                     expected_m=expected_m)
+        fp8_m_grouped_gemm_nt_masked((a2q, a2q_scale), (w2, w2_scale), output,
+                                     expert_num_tokens, expected_m)