diff --git a/backends/qualcomm/quantizer/custom_annotation.py b/backends/qualcomm/quantizer/custom_annotation.py
index e34630538d0..7063ab73aa3 100644
--- a/backends/qualcomm/quantizer/custom_annotation.py
+++ b/backends/qualcomm/quantizer/custom_annotation.py
@@ -11,6 +11,7 @@
     _is_float_tensor,
     Q_ANNOTATION_KEY,
 )
+from enum import Enum, unique
 from executorch.backends.qualcomm.quantizer.quantizer import (
     get_16a8w_qnn_ptq_config,
     get_16a8w_qnn_qat_config,
@@ -125,6 +126,100 @@ def annotate_prefill_kv_output(gm: torch.fx.GraphModule, kv_quant_attrs: dict):
                     _annotated=True,
                 )
 
+def annotate_output_16a8w(gm: torch.fx.GraphModule, is_qat: bool = False) -> None:
+    """
+    This function is for static LLM models.
+    This function will annotate the last conv(linear), which is the lm_head, as 16a8w.
+    """
+
+    def annotate_conv2d(node: Node, quantization_config: QuantizationConfig) -> None:
+        input_qspec_map = {}
+        input_act = node.args[0]
+        input_spec = quantization_config.input_activation
+        input_qspec_map[input_act] = input_spec
+
+        weight = node.args[1]
+        input_qspec_map[weight] = quantization_config.weight
+
+        if len(node.args) > 2 and isinstance(node.args[2], Node):
+            input_qspec_map[node.args[2]] = quantization_config.bias(node)
+
+        node.meta[Q_ANNOTATION_KEY] = QuantizationAnnotation(
+            input_qspec_map=input_qspec_map,
+            output_qspec=quantization_config.output_activation,
+            _annotated=True,
+        )
+
+    if is_qat:
+        quantization_config_16a8w_per_channel = get_qat_per_channel_quant_config(
+            torch.uint16, weight_dtype=torch.int8, act_observer=MinMaxObserver
+        )
+    else:
+        quantization_config_16a8w_per_channel = get_ptq_per_channel_quant_config(
+            torch.uint16, weight_dtype=torch.int8, act_observer=MinMaxObserver
+        )
+    for node in gm.graph.nodes:
+        if node.op == "call_function" and node.target == torch.ops.aten.conv2d.default:
+            if "nn_module_stack" in node.meta:
+                module_values_list = list(node.meta["nn_module_stack"].values())
+                full_qualified_name = module_values_list[-1][0]
+                if full_qualified_name == "output.conv":
+                    annotate_conv2d(
+                        node, quantization_config=quantization_config_16a8w_per_channel
+                    )
+
+
+@unique
+class StaticLLMQuantConfig(Enum):
+    """
+    Layer namespace configuration for Qualcomm's static LLaMA quantization.
+    """
+
+    wq_sha = "wq_sha"  # Query weight (single head)
+    wk_sha = "wk_sha"  # Key weight (single head)
+    wv_sha = "wv_sha"  # Value weight (single head)
+
+def annotate_qkv_proj_sha(
+    gm: torch.fx.GraphModule,
+    quantization_config: QuantizationConfig,
+    qkv_tags: set[StaticLLMQuantConfig],
+):
+    """
+    Annotates QKV projection layers in a GraphModule for quantization,
+    specifically layers defined in StaticLLMQuantConfig.
+
+    Args:
+        qkv_tags (set[StaticLLMQuantConfig]): A set of enum tags indicating which QKV layers
+            (e.g., wq, wk, wv) should be annotated for quantization. Only tags defined in
+            StaticLLMQuantConfig are allowed.
+
+    Raises:
+        ValueError: If any tag in `qkv_tags` is not among the allowed enum members.
+    """
+
+    # Get all valid tags from the StaticLLMQuantConfig enum
+    allowed_tags = set(StaticLLMQuantConfig)
+    invalid_tags = qkv_tags - allowed_tags
+    if invalid_tags:
+        raise ValueError(
+            f"Invalid qkv tags: {invalid_tags}. Allowed tags are: {allowed_tags}"
+        )
+
+    for node in gm.graph.nodes:
+        if node.target == torch.ops.aten.conv2d.default and any(
+            tag.value in node.meta["stack_trace"] for tag in qkv_tags
+        ):
+            input_qspec_map = {}
+            input_qspec_map[node.args[0]] = quantization_config.input_activation
+            input_qspec_map[node.args[1]] = quantization_config.weight
+            if len(node.args) > 2 and isinstance(node.args[2], Node):
+                input_qspec_map[node.args[2]] = quantization_config.bias(node)
+            node.meta[Q_ANNOTATION_KEY] = QuantizationAnnotation(
+                input_qspec_map=input_qspec_map,
+                output_qspec=quantization_config.output_activation,
+                _annotated=True,
+            )
+
 
 def annotate_kv_8bit(  # noqa: C901
     gm: torch.fx.GraphModule,