diff --git a/backends/arm/test/models/test_nss.py b/backends/arm/test/models/test_nss.py
index d738ac73f15..1eb2805b086 100644
--- a/backends/arm/test/models/test_nss.py
+++ b/backends/arm/test/models/test_nss.py
@@ -23,7 +23,6 @@
     REAL_AND_RANDOM_DATA,
     skip_if_frozen_release,
 )
-from executorch.backends.arm.test.tester.quantize import ArmQuantize
 from executorch.backends.arm.test.tester.test_pipeline import (
     EthosU55PipelineINT,
     EthosU85PipelineINT,
@@ -65,32 +64,6 @@ def __init__(self, *args, **kwargs):
         self.auto_encoder = AutoEncoderV1()
 
 
-class _Fp64ConvReference(torch.fx.Interpreter):
-    def call_function(self, target, args, kwargs):
-        if target == torch.ops.aten.conv2d.default:
-            x, weight, bias, *options = args
-            return target(
-                x.double(),
-                weight.double(),
-                bias.double() if bias is not None else None,
-                *options,
-                **kwargs,
-            ).to(x.dtype)
-        return super().call_function(target, args, kwargs)
-
-
-class _NssFp64ReferenceQuantize(ArmQuantize):
-    # TODO(MLETORCH-2609): FP32 bias accumulation changes quantization decisions
-    # across hosts. Use FP64 only for the quantized reference's convolutions.
-    def run_artifact(self, inputs):
-        conv_count = sum(
-            node.op == "call_function" and node.target == torch.ops.aten.conv2d.default
-            for node in self.artifact.graph.nodes
-        )
-        assert conv_count == 14, f"Expected 14 NSS conv2d nodes, found {conv_count}"
-        return _Fp64ConvReference(self.artifact).run(*inputs)
-
-
 def nss() -> AutoEncoderV1:
     """Get an instance of NSS with weights loaded."""
 
@@ -227,21 +200,6 @@ def test_nss_tosa_INT(use_real_data, is_qat):
     )
     if use_real_data:
         _set_nss_calibration_samples(pipeline)
-    elif not is_qat:
-        quantize_stage = pipeline._stages[pipeline.find_pos("quantize")].args[0]
-        pipeline.change_args(
-            "quantize",
-            _NssFp64ReferenceQuantize(
-                quantizer=quantize_stage.quantizer,
-                quantization_config=quantize_stage.quantization_config,
-                calibrate=quantize_stage.calibrate,
-                calibration_samples=quantize_stage.calibration_samples,
-                is_qat=quantize_stage.is_qat,
-                set_global=False,
-                fold_quantize=quantize_stage.fold_quantize,
-                dynamic_shapes=quantize_stage.dynamic_shapes,
-            ),
-        )
     pipeline.run()