diff --git a/python/perf-kernels/rmsnorm.py b/python/perf-kernels/rmsnorm.py
index 7aa247761cbd..0df4e2a62517 100644
--- a/python/perf-kernels/rmsnorm.py
+++ b/python/perf-kernels/rmsnorm.py
@@ -46,35 +46,47 @@ def get_autotune_config():
 
 @triton.autotune(configs=get_autotune_config(), key=['n_rows', 'n_cols'], use_cuda_graph=True)
 @triton.jit
-def rms_kernel(output_ptr, input_ptr, g_ptr, input_row_stride, output_row_stride, n_rows, n_cols, epsilon,
+def rms_kernel(output_ptr, input_ptr, g_ptr, input_row_stride, output_row_stride, n_rows, n_cols, eps,
                BLOCK_SIZE: tl.constexpr):
     row_start = tl.program_id(0)
-    row_step = tl.num_programs(0)
-    col_offsets = tl.arange(0, BLOCK_SIZE)
-    mask = col_offsets < n_cols
-    for row_idx in tl.range(row_start, n_rows, row_step):
-        row_start_ptr = input_ptr + row_idx * input_row_stride
+    row_idx = row_start
+
+    #Calculate squared mean by block
+    row_start_ptr = input_ptr + row_idx * input_row_stride
+    row_sum = 0.0
+    for b in tl.range(0, n_cols, BLOCK_SIZE):
+        col_offsets = b + tl.arange(0, BLOCK_SIZE)
+        input_ptrs = row_start_ptr + col_offsets
+        mask = col_offsets < n_cols
+        row_block = tl.load(input_ptrs, mask=mask, other=0.0, cache_modifier=".cg")
+        row_block = row_block * row_block  #square every value the block
+        row_sum += (tl.sum(row_block, axis=-1) / n_cols
+                    )  #tl.sum across row, divide by block_size and add it running sum
+
+    row_norm = row_sum + eps
+    row_norm = tl.rsqrt(row_norm)
+
+    #Blocked normalization
+    output_row_start_ptr = output_ptr + row_idx * output_row_stride
+    for b in tl.range(0, n_cols, BLOCK_SIZE):
+        col_offsets = b + tl.arange(0, BLOCK_SIZE)
         input_ptrs = row_start_ptr + col_offsets
-        input_ptrs = tl.multiple_of(input_ptrs, (16, ))
-        row = tl.load(input_ptrs, mask=mask, other=0.0, cache_modifier=".cg")
-        g = tl.load(g_ptr + col_offsets, mask=mask, other=0.0, cache_modifier=".cg")
-        row_norm = row * row  #square each value
-        row_norm = tl.sum(row_norm, axis=-1)  #sum across columns(axis=-1)
-        row_norm = row_norm / n_cols  #divide by n_cols
-        row_norm = row_norm + epsilon  #add epsilon
-        row_norm = tl.rsqrt(row_norm)  #take rsqrt, this is normalization value
-        rms_norm = row * row_norm  #multiply each x by normalization value
-        rms_norm = rms_norm * g  #element wise multiplication with g
-
-        output_row_start_ptr = output_ptr + row_idx * output_row_stride
+        mask = col_offsets < n_cols
+        row_block = tl.load(input_ptrs, mask=mask, other=0.0, cache_modifier=".cg")  #load block of input
+        g = tl.load(g_ptr + col_offsets, mask=mask, other=0.0, cache_modifier=".cg")  #load block of g
+        output = row_block * row_norm  #element wise multiply with rms_norm
+        output = output * g  #element wise multiplication with g
+
         output_ptrs = output_row_start_ptr + col_offsets
-        output_ptrs = tl.multiple_of(output_ptrs, (16, ))
-        tl.store(output_ptrs, rms_norm, mask=mask)
+        tl.store(output_ptrs, output, mask=mask)
 
 
 def rmsnorm(x, epsilon=1e-6):
     n_rows, n_cols = x.shape
-    BLOCK_SIZE = triton.next_power_of_2(n_cols)
+    #Restricting BLOCK_SIZE to 64Kb is an important optimization. Otherwise,
+    #performance can drop significantly for larger n_cols.
+    MAX_FUSED_SIZE = 65536 // x.element_size()
+    BLOCK_SIZE = min(MAX_FUSED_SIZE, triton.next_power_of_2(n_cols))
 
     y = torch.empty_like(x, device='cuda')
     g = torch.ones((1, n_cols), device='cuda')
@@ -87,6 +99,7 @@ def rmsnorm(x, epsilon=1e-6):
 
 
 def run_rmsnorm(M, N):
+    print(f"Running RMSNorm for shape ({M}, {N})")
     torch.manual_seed(0)
     x = torch.randn(M, N, device='cuda')
     y_triton = rmsnorm(x)
@@ -94,14 +107,7 @@ def run_rmsnorm(M, N):
     return y_triton
 
 
-@pytest.mark.parametrize('M, N', [
-    (1, 4),
-    (2, 10),
-    (8192, 4096),
-    (4096, 8192),
-    (1, 8192),
-    (873, 1245),
-])
+@pytest.mark.parametrize('M, N', [(1, 4), (2, 10), (8192, 4096), (4096, 8192), (1, 8192), (873, 1245), (1, 98304)])
 def test_rmsnorm(M, N):
     torch.manual_seed(0)
     x = torch.randn(M, N, device='cuda')
@@ -110,6 +116,7 @@ def test_rmsnorm(M, N):
     rms_norm = torch.nn.RMSNorm(N, device='cuda')
     y_torch = rms_norm(x)
 
+    print(f"y_triton={y_triton}")
     assert torch.allclose(y_triton, y_torch), (y_triton, y_torch)