diff --git a/tests/brevitas_examples/test_llm.py b/tests/brevitas_examples/test_llm.py
index 60dd33ac2..ac9bd6ec2 100644
--- a/tests/brevitas_examples/test_llm.py
+++ b/tests/brevitas_examples/test_llm.py
@@ -2,6 +2,7 @@
 # SPDX-License-Identifier: BSD-3-Clause
 
 from argparse import Namespace
+from collections import defaultdict
 from dataclasses import dataclass
 import logging
 import os
@@ -72,6 +73,19 @@ def assert_layer_types(model, exp_layer_types):
         assert matched, f"Layer key: {key} not found in {layer_names}"
 
 
+def assert_layer_types_count(model, exp_layer_types_count):
+    layer_types_count = {}
+    for name, layer in model.named_modules():
+        ltype = str(type(layer))
+        if ltype not in layer_types_count:
+            layer_types_count[ltype] = 0
+        layer_types_count[ltype] += 1
+
+    for name, count in exp_layer_types_count.items():
+        curr_count = 0 if name not in layer_types_count else layer_types_count[name]
+        assert count == curr_count, f"Expect {count} instances of layer type: {name}, found {curr_count}."
+
+
 class UpdatableNamespace(Namespace):
 
     def update(self, **kwargs):
@@ -277,6 +291,15 @@ def test_small_models_acc_pt_ge_2_4(caplog, acc_args_and_acc_pt_ge_2_4):
     assert allveryclose(exp_quant_ppl, quant_ppl), f"Expected quant PPL {exp_quant_ppl}, measured PPL {quant_ppl}"
 
 
+def llama_module_type_count(num_hidden_layers: int):
+    return {
+        "<class 'torch.nn.modules.linear.Linear'>": 1 + num_hidden_layers *
+                                                    (4 + 3),  # lm_head + (k/q/v/o_proj + MLP)
+        "<class 'torch.nn.modules.normalization.RMSNorm'>":
+            1 + num_hidden_layers * 2,  # input + post_attention
+    }
+
+
 @pytest_cases.fixture(
     ids=[
         "mistral-int8",
@@ -299,7 +322,13 @@ def test_small_models_acc_pt_ge_2_4(caplog, acc_args_and_acc_pt_ge_2_4):
                 "model.layers.0.self_attn.q_proj.input_quant.fused_activation_quant_proxy.tensor_quant":
                     "<class 'brevitas.core.quant.int.RescalingIntQuant'>",
                 "model.layers.0.self_attn.q_proj.weight_quant.tensor_quant":
-                    "<class 'brevitas.core.quant.int.RescalingIntQuant'>",}},
+                    "<class 'brevitas.core.quant.int.RescalingIntQuant'>",},
+            "exp_layer_types_count": {
+                "<class 'torch.nn.modules.linear.Linear'>": 1,  # LM Head
+                "<class 'brevitas.nn.quant_linear.QuantLinear'>":
+                    14,  # Q/K/V/O projs + Up/Gate/Down projs
+                "<class 'brevitas.core.quant.int.RescalingIntQuant'>": 28,
+            }},  # input_quant/weight_quant
         {
             "model": "hf-internal-testing/tiny-random-MistralForCausalLM",
             "input_bit_width": None,
@@ -310,7 +339,13 @@ def test_small_models_acc_pt_ge_2_4(caplog, acc_args_and_acc_pt_ge_2_4):
                 "model.layers.0.self_attn.q_proj.input_quant":
                     "<class 'brevitas.proxy.runtime_quant.ActQuantProxyFromInjector'>",
                 "model.layers.0.self_attn.q_proj.weight_quant.tensor_quant":
-                    "<class 'brevitas.core.quant.int.RescalingIntQuant'>",}},
+                    "<class 'brevitas.core.quant.int.RescalingIntQuant'>",},
+            "exp_layer_types_count": {
+                "<class 'torch.nn.modules.linear.Linear'>": 1,  # LM Head
+                "<class 'brevitas.nn.quant_linear.QuantLinear'>":
+                    14,  # Q/K/V/O projs + Up/Gate/Down projs
+                "<class 'brevitas.core.quant.int.RescalingIntQuant'>": 14,
+            }},  # input_quant/weight_quant
         {
             "model": "hf-internal-testing/tiny-random-MistralForCausalLM",
             "weight_quant_format": "float_ocp_e4m3",
@@ -323,7 +358,12 @@ def test_small_models_acc_pt_ge_2_4(caplog, acc_args_and_acc_pt_ge_2_4):
                 "model.layers.0.self_attn.q_proj.input_quant.fused_activation_quant_proxy.tensor_quant":
                     "<class 'brevitas.core.quant.float.FloatQuant'>",
                 "model.layers.0.self_attn.q_proj.weight_quant.tensor_quant":
-                    "<class 'brevitas.core.quant.float.FloatQuant'>",}},
+                    "<class 'brevitas.core.quant.float.FloatQuant'>",},
+            "exp_layer_types_count": {
+                "<class 'torch.nn.modules.linear.Linear'>": 1,  # LM Head
+                "<class 'brevitas.nn.quant_linear.QuantLinear'>":
+                    14,  # Q/K/V/O projs + Up/Gate/Down projs
+                "<class 'brevitas.core.quant.float.FloatQuant'>": 28,}},  # input_quant/weight_quant
         {
             "model": "hf-internal-testing/tiny-random-MistralForCausalLM",
             "weight_quant_format": "float_fnuz_e4m3",
@@ -336,7 +376,12 @@ def test_small_models_acc_pt_ge_2_4(caplog, acc_args_and_acc_pt_ge_2_4):
                 "model.layers.0.self_attn.q_proj.input_quant.fused_activation_quant_proxy.tensor_quant":
                     "<class 'brevitas.core.quant.float.FloatQuant'>",
                 "model.layers.0.self_attn.q_proj.weight_quant.tensor_quant":
-                    "<class 'brevitas.core.quant.float.FloatQuant'>",}},
+                    "<class 'brevitas.core.quant.float.FloatQuant'>",},
+            "exp_layer_types_count": {
+                "<class 'torch.nn.modules.linear.Linear'>": 1,  # LM Head
+                "<class 'brevitas.nn.quant_linear.QuantLinear'>":
+                    14,  # Q/K/V/O projs + Up/Gate/Down projs
+                "<class 'brevitas.core.quant.float.FloatQuant'>": 28,}},  # input_quant/weight_quant
         {
             "model": "hf-internal-testing/tiny-random-LlamaForCausalLM",
             "weight_quant_format": "float_ocp_e4m3",
@@ -363,7 +408,17 @@ def test_small_models_acc_pt_ge_2_4(caplog, acc_args_and_acc_pt_ge_2_4):
                 "model.layers.0.self_attn.q_proj.weight_quant.tensor_quant":
                     "<class 'brevitas.core.quant.float.FloatQuant'>",
                 "model.layers.0.self_attn.q_proj.weight_quant.tensor_quant.input_view_impl":
-                    "<class 'brevitas.core.function_wrapper.shape.OverSubChannelBlockView'>",}},
+                    "<class 'brevitas.core.function_wrapper.shape.OverSubChannelBlockView'>",},
+            "exp_layer_types_count": {
+                "<class 'brevitas.nn.quant_linear.QuantLinear'>":
+                    14,  # Q/K/V/O projs + Up/Gate/Down projs
+                "<class 'brevitas.core.quant.float.FloatQuant'>": 28,  # input_quant/weight_quant
+                "<class 'brevitas.core.function_wrapper.shape.DynamicOverSubChannelBlockView'>":
+                    14,  # input_quant..input_view_impl/input_quant..scaling_impl.input_view_impl
+                "<class 'brevitas.core.function_wrapper.shape.OverSubChannelBlockView'>":
+                    28,  # weight_quant..input_view_impl/weight_quant..scaling_impl.input_view_impl
+                "<class 'torch.nn.modules.linear.Linear'>": 1,  # LM Head
+                "<class 'transformers.models.llama.modeling_llama.LlamaRMSNorm'>": 5,}},
         {
             "model": "hf-internal-testing/tiny-random-LlamaForCausalLM",
             "act_equalization": "layerwise",
@@ -371,12 +426,22 @@ def test_small_models_acc_pt_ge_2_4(caplog, acc_args_and_acc_pt_ge_2_4):
                 "model.layers.0.self_attn.q_proj":
                     "<class 'brevitas.nn.equalized_layer.EqualizedModule'>",
                 "model.layers.0.self_attn.q_proj.layer":
-                    "<class 'brevitas.nn.quant_linear.QuantLinear'>",}},
+                    "<class 'brevitas.nn.quant_linear.QuantLinear'>",},
+            "exp_layer_types_count": {
+                "<class 'brevitas.nn.quant_linear.QuantLinear'>":
+                    14,  # Q/K/V/O projs + Up/Gate/Down projs
+                "<class 'torch.nn.modules.linear.Linear'>": 1,  # LM Head
+                "<class 'brevitas.nn.equalized_layer.EqualizedModule'>":
+                    15,  # LM Head + Q/K/V/O projs + Up/Gate/Down projs
+                "<class 'transformers.models.llama.modeling_llama.LlamaRMSNorm'>": 5,}},
         {
             "model": "hf-internal-testing/tiny-random-MistralForCausalLM",
             "quantize_last_layer": True,
             "exp_layer_types": {
-                "lm_head": "<class 'brevitas.nn.quant_linear.QuantLinear'>"}},
+                "lm_head": "<class 'brevitas.nn.quant_linear.QuantLinear'>"},
+            "exp_layer_types_count": {
+                "<class 'brevitas.nn.quant_linear.QuantLinear'>": 15,
+            }},  # LM Head + Q/K/V/O projs + Up/Gate/Down projs
         {
             "model": "hf-internal-testing/tiny-random-LlamaForCausalLM",
             "ln_affine_merge": True,
@@ -390,7 +455,14 @@ def test_small_models_acc_pt_ge_2_4(caplog, acc_args_and_acc_pt_ge_2_4):
                 "L__self___model_layers_0_self_attn_k_proj":
                     "<class 'torch.nn.modules.linear.Linear'>",
                 "L__self___model_layers_0_self_attn_o_proj":
-                    "<class 'brevitas.nn.equalized_layer.RotatedModule'>"}},
+                    "<class 'brevitas.nn.equalized_layer.RotatedModule'>",},
+            "exp_layer_types_count": {
+                "<class 'brevitas.nn.equalized_layer.RotatedModule'>":
+                    4,  # Sinks: O proj + Down proj
+                "<class 'torch.nn.modules.linear.Linear'>":
+                    15,  # LM Head + Q/K/V/O projs + Up/Gate/Down projs
+                "<class 'torch.nn.modules.normalization.RMSNorm'>": 5,
+                "<class 'torch.nn.modules.normalization.LayerNorm'>": 0,}},
         {
             "model": "hf-internal-testing/tiny-random-LlamaForCausalLM",
             "ln_affine_merge": True,
@@ -404,28 +476,39 @@ def test_small_models_acc_pt_ge_2_4(caplog, acc_args_and_acc_pt_ge_2_4):
                 "L__self___model_layers_0_self_attn_k_proj":
                     "<class 'torch.nn.modules.linear.Linear'>",
                 "L__self___model_layers_0_self_attn_o_proj":
-                    "<class 'torch.nn.modules.linear.Linear'>"}},])
+                    "<class 'torch.nn.modules.linear.Linear'>"},
+            "exp_layer_types_count": {
+                "<class 'torch.nn.modules.linear.Linear'>":
+                    15,  # LM Head + Q/K/V projs + Up/Gate/Down projs
+                "<class 'torch.nn.modules.normalization.RMSNorm'>": 5,  # Input + Post attention
+                "<class 'torch.nn.modules.normalization.LayerNorm'>": 0,}},])
 def layer_args(default_run_args, request):
     args = default_run_args
     layer_dict = request.param
     exp_layer_types = layer_dict["exp_layer_types"]
+    exp_layer_types_count = layer_dict["exp_layer_types_count"]
     del layer_dict["exp_layer_types"]
+    del layer_dict["exp_layer_types_count"]
     args.update(**layer_dict)
-    yield args, exp_layer_types
+    yield args, exp_layer_types, exp_layer_types_count
 
 
-@pytest.mark.llm
-@requires_pt_ge('2.2')
 def test_small_models_quant_layer(caplog, layer_args):
     caplog.set_level(logging.INFO)
-    args, exp_layer_types = layer_args
+    args, exp_layer_types, exp_layer_types_count = layer_args
     if args.replace_rmsnorm:
         if torch_version < version.parse('2.4'):
             pytest.skip("Replacing RMSNorm requires torch 2.4+ or greater")
         if hasattr(args, 'rotation') and args.rotation == 'fx' and platform.system() == 'Windows':
             pytest.skip("Skipping dynamo + windows")
     float_ppl, quant_ppl, model = validate_args_and_run_main(args)
-    assert_layer_types(model, exp_layer_types)
+    # Naming of modules in the GraphModule generated by FX changes across transformers versions, e.g.
+    # (4.45.0)"L__self___model_layers_2_self_attn_k_proj" ->
+    # (4.46.0) 'L__self___model_layers_slice_None__2__None___0_self_attn_q_proj'
+    # Therefore, this check is skipped when rotation="fx".
+    if args.rotation != "fx":
+        assert_layer_types(model, exp_layer_types)
+    assert_layer_types_count(model, exp_layer_types_count)
 
 
 @pytest_cases.fixture(