diff --git a/vllm/patches/deepseek_v4.py b/vllm/patches/deepseek_v4.py
index 1227e79b..6a0c4d5c 100644
--- a/vllm/patches/deepseek_v4.py
+++ b/vllm/patches/deepseek_v4.py
@@ -1197,11 +1197,7 @@ class DeepseekV4DecoderLayer(nn.Module):
             x, self.hc_attn_fn, self.hc_attn_scale, self.hc_attn_base
         )
         x = self.attn_norm(x)
-        if os.environ.get('CLAWMINE_DEBUG', '0') == '1':
-            _print_tensor("pre_attn", x, self._layer_idx if hasattr(self, '_layer_idx') else -1)
         x = self.attn(positions, x, None)
-        if os.environ.get('CLAWMINE_DEBUG', '0') == '1':
-            _print_tensor("post_attn", x, self._layer_idx if hasattr(self, '_layer_idx') else -1)
         x = self.hc_post(x, residual, post, comb)
 
         residual = x
@@ -1214,26 +1210,6 @@ class DeepseekV4DecoderLayer(nn.Module):
         return x
 
 
-def _print_tensor(label, t, layer_idx):
-    with torch.no_grad():
-        print(f"[CLAWMINE] L{layer_idx} {label}: amax={t.amax().item():.4f} NaN={torch.isnan(t).any().item()} shape={t.shape}")
-
-
-def _diag_hidden_stats(hidden_states: torch.Tensor, layer_idx: int):
-    """Print hidden state stats after each layer. Disabled unless
-    CLAWMINE_DEBUG=1. os.environ is evaluated at trace time, so
-    the data-dependent path is dead code when disabled."""
-    if os.environ.get('CLAWMINE_DEBUG', '0') != '1':
-        return
-    # Only reached when CLAWMINE_DEBUG=1 (must run with --enforce-eager)
-    with torch.no_grad():
-        amax = hidden_states.amax().item()
-        mean = hidden_states.float().mean().item()
-        has_nan = torch.isnan(hidden_states).any().item()
-        has_inf = torch.isinf(hidden_states).any().item()
-        print(f"[CLAWMINE] Layer {layer_idx}: amax={amax:.4f} mean={mean:.6f} NaN={has_nan} Inf={has_inf}")
-
-
 @support_torch_compile
 class DeepseekV4Model(nn.Module):
     def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
@@ -1332,19 +1308,14 @@ class DeepseekV4Model(nn.Module):
     ) -> torch.Tensor | IntermediateTensors:
         hidden_states = self.embed_input_ids(input_ids)
         hidden_states = hidden_states.unsqueeze(-2).repeat(1, self.hc_mult, 1)
-        if os.environ.get('CLAWMINE_DEBUG', '0') == '1':
-            _print_tensor("embed", hidden_states, -1)
         if self.use_mega_moe:
             input_ids = input_ids.to(torch.int64)
         for layer_idx, layer in enumerate(islice(self.layers, self.start_layer, self.end_layer)):
-            layer._layer_idx = layer_idx
             hidden_states = layer(
                 hidden_states,
                 positions,
                 input_ids,
             )
-            # Diagnostic: print amax/mean every layer (eager-mode only, no Dynamo)
-            _diag_hidden_stats(hidden_states, layer_idx)
 
 
         # Stash pre-hc_head residual for the MTP draft (captured copy_).
diff --git a/vllm/patches/deepseek_v4_attention.py b/vllm/patches/deepseek_v4_attention.py
index 3e0ed6c2..2bde4a53 100644
--- a/vllm/patches/deepseek_v4_attention.py
+++ b/vllm/patches/deepseek_v4_attention.py
@@ -3,7 +3,6 @@
 """
 DeepseekV4 MLA Attention Layer
 """
-import os
 
 from collections.abc import Callable
 from dataclasses import dataclass
@@ -304,10 +303,6 @@ class DeepseekV4MultiHeadLatentAttentionWrapper(PluggableLayer):
         )
         o = o_padded[:, : self.n_local_heads, :]
 
-        if os.environ.get('CLAWMINE_DEBUG', '0') == '1':
-            with torch.no_grad():
-                print(f"[CLAWMINE] attn L{self.layer_name}: o amax={o.amax().item():.4f} NaN={torch.isnan(o).any().item()}")
-
         # O projection: inverse RoPE + FP8 quant + einsum + wo_b
         o_fp8, o_scale = fused_inv_rope_fp8_quant(
             o,
@@ -320,10 +315,6 @@ class DeepseekV4MultiHeadLatentAttentionWrapper(PluggableLayer):
             tma_aligned_scales=self._tma_aligned_scales,
         )
 
-        if os.environ.get('CLAWMINE_DEBUG', '0') == '1':
-            with torch.no_grad():
-                print(f"[CLAWMINE] attn L{self.layer_name}: o_fp8 amax={o_fp8.float().amax().item():.4f} o_scale amax={o_scale.float().amax().item():.6f}")
-
         wo_a_fp8 = self.wo_a.weight
         wo_a_scale = self.wo_a.weight_scale_inv