diff --git a/cutedsl/bridge.py b/cutedsl/bridge.py
index b20a2deb..2849ceb3 100644
--- a/cutedsl/bridge.py
+++ b/cutedsl/bridge.py
@@ -99,13 +99,18 @@ def quantize_to_nvfp4(x_bf16, block_size=SF_VEC_SIZE):
 
     x_reshaped = x_norm.reshape(*x_norm.shape[:-1], n_blocks, block_size)
     block_amax = x_reshaped.abs().amax(dim=-1)
-    zero_block = block_amax == 0
-    # Clamp for safe division; zero blocks get scale=0 after the where.
+    # Detect zero blocks and underflow blocks (amax > 0 but too small for FP8).
+    # Smallest positive FP8 e4m3fn is 2^-9 ≈ 1.95e-3. If amax/6 < this,
+    # the block scale underflows to 0, and dividing x by the clamped 1e-8
+    # inflates values into nonzero FP4 buckets — producing wrong results.
+    zero_block = block_amax < (6.0 * 2.0 ** -9)  # < ~0.0117
+    # Zero out x for zero/underflow blocks before division.
+    # This ensures x_scaled = 0 → FP4 nibbles = 0.
+    x_reshaped = torch.where(zero_block.unsqueeze(-1),
+                              torch.zeros_like(x_reshaped), x_reshaped)
     block_amax = block_amax.clamp(min=1e-8)
     block_scale = (block_amax / 6.0).to(torch.float8_e4m3fn)
-    # Zero blocks: force both FP4 nibbles and FP8 scale to exact zero.
-    # Without this, the FP8 scale underflows to 0 anyway for small blocks
-    # but the div-by-tiny-number can produce nonzero FP4 from noise.
+    # Force zero/underflow blocks: FP8 scale = 0 (exact zero).
     block_scale = torch.where(zero_block, torch.zeros_like(block_scale), block_scale)
 
     # Nearest E2M1
@@ -162,10 +167,12 @@ def quantize_activation_nvfp4(x_bf16, global_scale, block_size=SF_VEC_SIZE):
 
     x_reshaped = x_norm.reshape(*x_norm.shape[:-1], n_blocks, block_size)
     block_amax = x_reshaped.abs().amax(dim=-1)
-    zero_block = block_amax == 0
+    # Detect zero blocks and underflow blocks (same threshold as quantize_to_nvfp4).
+    zero_block = block_amax < (6.0 * 2.0 ** -9)
+    x_reshaped = torch.where(zero_block.unsqueeze(-1),
+                              torch.zeros_like(x_reshaped), x_reshaped)
     block_amax = block_amax.clamp(min=1e-8)
     block_scale = (block_amax / 6.0).to(torch.float8_e4m3fn)
-    # Zero blocks: force both FP4 nibbles and FP8 scale to exact zero.
     block_scale = torch.where(zero_block, torch.zeros_like(block_scale), block_scale)
 
     block_sf_expanded = block_scale.float().unsqueeze(-1)
@@ -218,10 +225,12 @@ def quantize_weight_to_nvfp4(w_bf16, block_size=SF_VEC_SIZE):
 
     w_reshaped = w_norm.reshape(k_blocks, block_size, N)
     w_block_amax = w_reshaped.abs().amax(dim=1)
-    zero_block = w_block_amax == 0
+    # Detect zero blocks and underflow blocks (same threshold).
+    zero_block = w_block_amax < (6.0 * 2.0 ** -9)
+    w_reshaped = torch.where(zero_block.unsqueeze(1),
+                              torch.zeros_like(w_reshaped), w_reshaped)
     w_block_amax = w_block_amax.clamp(min=1e-8)
     w_sf = (w_block_amax / 6.0).to(torch.float8_e4m3fn)
-    # Zero blocks: force both FP4 nibbles and FP8 scale to exact zero.
     w_sf = torch.where(zero_block, torch.zeros_like(w_sf), w_sf)
 
     w_block_sf = w_sf.float().unsqueeze(1)