diff --git a/vllm/model_executor/models/exaone.py b/vllm/model_executor/models/exaone.py
index 0398f0943a70..8324a563edd6 100644
--- a/vllm/model_executor/models/exaone.py
+++ b/vllm/model_executor/models/exaone.py
@@ -606,8 +606,9 @@ class ExaoneForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
                 # which is consistent with the practice of setting
                 # scaling_factor = tensor_amax / FPtype_max
                 scaling_factor *= 2
-            if hasattr(layer_self_attn, "kv_scale"):
-                layer_self_attn.attn._kv_scale = scaling_factor
+            if hasattr(layer_self_attn.attn, "_k_scale"):
+                layer_self_attn.attn._k_scale = scaling_factor
+                layer_self_attn.attn._v_scale = scaling_factor
             else:
                 raise RuntimeError("Self attention has no KV cache scaling "
                                    "factor attribute!")
diff --git a/vllm/model_executor/models/granite.py b/vllm/model_executor/models/granite.py
index f9e0443b9a50..a91ed4158a73 100644
--- a/vllm/model_executor/models/granite.py
+++ b/vllm/model_executor/models/granite.py
@@ -545,8 +545,9 @@ class GraniteForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
                 # which is consistent with the practice of setting
                 # scaling_factor = tensor_amax / FPtype_max
                 scaling_factor *= 2
-            if hasattr(layer_self_attn, "kv_scale"):
-                layer_self_attn.attn._kv_scale = scaling_factor
+            if hasattr(layer_self_attn.attn, "_k_scale"):
+                layer_self_attn.attn._k_scale = scaling_factor
+                layer_self_attn.attn._v_scale = scaling_factor
             else:
                 raise RuntimeError("Self attention has no KV cache scaling "
                                    "factor attribute!")
diff --git a/vllm/model_executor/models/llama.py b/vllm/model_executor/models/llama.py
index 2902e6999c2f..8623da99574b 100644
--- a/vllm/model_executor/models/llama.py
+++ b/vllm/model_executor/models/llama.py
@@ -452,8 +452,9 @@ class LlamaModel(nn.Module):
                 # which is consistent with the practice of setting
                 # scaling_factor = tensor_amax / FPtype_max
                 scaling_factor *= 2
-            if hasattr(layer_self_attn, "kv_scale"):
-                layer_self_attn.attn._kv_scale = scaling_factor
+            if hasattr(layer_self_attn.attn, "_k_scale"):
+                layer_self_attn.attn._k_scale = scaling_factor
+                layer_self_attn.attn._v_scale = scaling_factor
             else:
                 raise RuntimeError("Self attention has no KV cache scaling "
                                    "factor attribute!")
diff --git a/vllm/model_executor/models/solar.py b/vllm/model_executor/models/solar.py
index caae0b65d7d1..a7cf65a0e36e 100644
--- a/vllm/model_executor/models/solar.py
+++ b/vllm/model_executor/models/solar.py
@@ -565,8 +565,9 @@ class SolarForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
                 # which is consistent with the practice of setting
                 # scaling_factor = tensor_amax / FPtype_max
                 scaling_factor *= 2
-            if hasattr(layer_self_attn, "kv_scale"):
-                layer_self_attn.attn._kv_scale = scaling_factor
+            if hasattr(layer_self_attn.attn, "_k_scale"):
+                layer_self_attn.attn._k_scale = scaling_factor
+                layer_self_attn.attn._v_scale = scaling_factor
             else:
                 raise RuntimeError("Self attention has no KV cache scaling "
                                    "factor attribute!")
diff --git a/vllm/worker/model_runner.py b/vllm/worker/model_runner.py
index 2b545d1b28bd..637fba23611f 100644
--- a/vllm/worker/model_runner.py
+++ b/vllm/worker/model_runner.py
@@ -1136,7 +1136,8 @@ class GPUModelRunnerBase(ModelRunnerBase[TModelInputForGPU]):
                 self.prompt_adapter_manager.create_prompt_adapter_manager(
                     self.model))
 
-        if self.kv_cache_dtype == "fp8" and current_platform.is_rocm():
+        if self.kv_cache_dtype == "fp8" and (current_platform.is_rocm()
+                                             or current_platform.is_cuda()):
             # Currently only ROCm accepts kv-cache scaling factors
             # via quantization_param_path and this will be deprecated
             # in the future.