diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py
index 9de2228b7..9b0fb5089 100644
--- a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py
+++ b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py
@@ -951,11 +951,11 @@ class CompressedTensorsKVCacheMethod(BaseKVCacheMethod):
                 f"received num_bits={num_bits}, type={type_}"
             )
 
-        # TODO: delegate validation to compressed-tensors library so that we have a
-        # single source of truth. Right now this is not possible until the next release
-        # of compressed-tensors.
-        strategy = kv_cache_scheme.get("strategy")
-        supported_strategies = ("tensor", "attn_head")
+        strategy = QuantizationStrategy(kv_cache_scheme.get("strategy"))
+        supported_strategies = (
+            QuantizationStrategy.TENSOR,
+            QuantizationStrategy.ATTN_HEAD,
+        )
         if strategy not in supported_strategies:
             raise NotImplementedError(
                 "Invalid strategy for compressed-tensors KV cache. "
@@ -981,9 +981,11 @@ class CompressedTensorsKVCacheMethod(BaseKVCacheMethod):
             hasattr(self.quant_config, "kv_cache_scheme")
             and self.quant_config.kv_cache_scheme is not None
         ):
-            strategy = self.quant_config.kv_cache_scheme["strategy"]
+            strategy = QuantizationStrategy(
+                self.quant_config.kv_cache_scheme["strategy"]
+            )
 
-        if strategy == "attn_head":
+        if strategy == QuantizationStrategy.ATTN_HEAD:
             assert layer.impl.supports_per_head_quant_scales, (
                 f"Layer {layer.__class__.__name__} with implementation "
                 f"{layer.impl.__class__.__name__} does not support per-head scales."
@@ -1020,7 +1022,7 @@ class CompressedTensorsKVCacheMethod(BaseKVCacheMethod):
         # - q_scale is partitioned over query heads.
         # - k/v_scale is partitioned over kv heads when total_kv_heads >= tp_size,
         #   and replicated when total_kv_heads < tp_size.
-        if strategy == "attn_head":
+        if strategy == QuantizationStrategy.ATTN_HEAD:
 
             def _tp_aware_loader(
                 param: torch.Tensor,