diff --git a/vllm/envs.py b/vllm/envs.py
index dc919e7741969..99f2ad2bc3d00 100755
--- a/vllm/envs.py
+++ b/vllm/envs.py
@@ -776,7 +776,7 @@ environment_variables: dict[str, Callable[[], Any]] = {
     # If set, the OpenAI API server will stay alive even after the underlying
     # AsyncLLMEngine errors and stops serving requests
     "VLLM_KEEP_ALIVE_ON_ENGINE_DEATH": lambda: bool(
-        os.getenv("VLLM_KEEP_ALIVE_ON_ENGINE_DEATH", 0)
+        int(os.getenv("VLLM_KEEP_ALIVE_ON_ENGINE_DEATH", "0"))
     ),
     # If the env var VLLM_ALLOW_LONG_MAX_MODEL_LEN is set, it allows
     # the user to specify a max sequence length greater than
@@ -1313,7 +1313,9 @@ environment_variables: dict[str, Callable[[], Any]] = {
     ),
     # If set, it means we pre-downloaded cubin files and flashinfer will
     # read the cubin files directly.
-    "VLLM_HAS_FLASHINFER_CUBIN": lambda: os.getenv("VLLM_HAS_FLASHINFER_CUBIN", False),
+    "VLLM_HAS_FLASHINFER_CUBIN": lambda: bool(
+        int(os.getenv("VLLM_HAS_FLASHINFER_CUBIN", "0"))
+    ),
     # Supported options:
     # - "flashinfer-cudnn": use flashinfer cudnn GEMM backend
     # - "flashinfer-trtllm": use flashinfer trtllm GEMM backend
@@ -1449,8 +1451,8 @@ environment_variables: dict[str, Callable[[], Any]] = {
     #                                      top 5 collected objects
     "VLLM_GC_DEBUG": lambda: os.getenv("VLLM_GC_DEBUG", ""),
     # Disables parallel execution of shared_experts via separate cuda stream
-    "VLLM_DISABLE_SHARED_EXPERTS_STREAM": lambda: os.getenv(
-        "VLLM_DISABLE_SHARED_EXPERTS_STREAM", False
+    "VLLM_DISABLE_SHARED_EXPERTS_STREAM": lambda: bool(
+        int(os.getenv("VLLM_DISABLE_SHARED_EXPERTS_STREAM", "0"))
     ),
     # Format for saving torch.compile cache artifacts
     # - "binary": saves as binary file