diff --git a/vllm/envs.py b/vllm/envs.py
index afae779fc80b2..ee4711dbec842 100644
--- a/vllm/envs.py
+++ b/vllm/envs.py
@@ -74,7 +74,6 @@
     VLLM_SYNC_SERVER_ENGINE_STEPS_BETWEEN_POLLS: int = 1
     VLLM_MOE_PADDING: bool = False
     VLLM_FP8_PADDING: bool = True
-    VLLM_NO_TUNED_GEMM: bool = False
 
 
 def get_default_cache_root():
@@ -487,10 +486,6 @@ def get_default_config_root():
     # Pad the weight for moe kernel or not
     "VLLM_FP8_PADDING":
     lambda: bool(int(os.getenv("VLLM_FP8_PADDING", "1"))),
-
-    # Not supported by mllama3.2
-    "VLLM_NO_TUNED_GEMM":
-    lambda: bool(int(os.getenv("VLLM_NO_TUNED_GEMM", "0"))),
 }
 
 # end-env-vars-definition
diff --git a/vllm/model_executor/layers/linear.py b/vllm/model_executor/layers/linear.py
index 01626236cc5a9..fce41b92d2fac 100644
--- a/vllm/model_executor/layers/linear.py
+++ b/vllm/model_executor/layers/linear.py
@@ -2,10 +2,8 @@
 from typing import Dict, List, Optional, Tuple
 
 import torch
-import torch.nn.functional as F
 from torch.nn.parameter import Parameter, UninitializedParameter
 
-import vllm.envs as envs
 from vllm.distributed import (divide, get_tensor_model_parallel_rank,
                               get_tensor_model_parallel_world_size,
                               split_tensor_along_last_dim,
@@ -133,10 +131,7 @@ def apply(self,
               layer: torch.nn.Module,
               x: torch.Tensor,
               bias: Optional[torch.Tensor] = None) -> torch.Tensor:
-        if envs.VLLM_NO_TUNED_GEMM:
-            return F.linear(x, layer.weight, bias)
-        else:
-            return tgemm.mm(x, layer.weight, bias)
+        return tgemm.mm(x, layer.weight, bias)
 
 
 class LinearBase(torch.nn.Module):
diff --git a/vllm/model_executor/layers/tuned_gemm.py b/vllm/model_executor/layers/tuned_gemm.py
index 7ea1d8d93ea2b..fc58c8c6d379e 100644
--- a/vllm/model_executor/layers/tuned_gemm.py
+++ b/vllm/model_executor/layers/tuned_gemm.py
@@ -84,7 +84,7 @@ def mm(self, inp, weights, bias=None):
         # uses this for linear units. However, sampler
         # will use torch.matmul with 2 dimensions only
         if inp.dim() == 3:
-            inp_view = inp.view(-1, inp.size(-1))
+            inp_view = inp.reshape(-1, inp.size(-1))
             batched = True
         else:
             inp_view = inp