diff --git a/modelopt/torch/export/unified_export_hf.py b/modelopt/torch/export/unified_export_hf.py
index b46f2dd70..d2c19b8e1 100644
--- a/modelopt/torch/export/unified_export_hf.py
+++ b/modelopt/torch/export/unified_export_hf.py
@@ -56,9 +56,6 @@
     set_expert_quantizer_amax,
 )
 from .model_config import (
-    KV_CACHE_FP8,
-    KV_CACHE_NVFP4,
-    KV_CACHE_NVFP4_AFFINE,
     QUANTIZATION_FP8,
     QUANTIZATION_FP8_PB_REAL,
     QUANTIZATION_FP8_PC_PT,
@@ -647,19 +644,6 @@ def _export_transformers_checkpoint(
 
     quant_config = get_quant_config(model, is_modelopt_qlora=is_modelopt_qlora)
 
-    kv_cache_max_bound = 0
-    kv_cache_format = quant_config["quantization"]["kv_cache_quant_algo"]
-
-    cache_bound_mapping = {
-        KV_CACHE_NVFP4: 6 * 448,
-        KV_CACHE_NVFP4_AFFINE: 6 * 448,
-        KV_CACHE_FP8: 448,
-    }
-
-    # Only update kv_cache_max_bound if a quantization is applied.
-    if kv_cache_format != QUANTIZATION_NONE:
-        kv_cache_max_bound = cache_bound_mapping.get(kv_cache_format)
-
     # Process all quantized modules and export weights
     _process_quantized_modules(model, dtype, is_modelopt_qlora)
 
@@ -669,6 +653,9 @@ def _export_transformers_checkpoint(
     else:
         quantized_state_dict = model.state_dict()
 
+    # We define kv cache scale as amax / 448 for both FP8 and NVFP4 KV cache quantization.
+    kv_cache_max_bound = 448
+    kv_cache_format = quant_config["quantization"]["kv_cache_quant_algo"]
     quantized_state_dict = postprocess_state_dict(
         quantized_state_dict, kv_cache_max_bound, kv_cache_format, is_modelopt_qlora
     )