Environment Variables#

vLLM uses the following environment variables to configure the system:


environment_variables: Dict[str, Callable[[], Any]] = {

    # ================== Installation Time Env Vars ==================

    # Target device of vLLM, supporting [cuda (by default), rocm, neuron, cpu]
    "VLLM_TARGET_DEVICE":
    lambda: os.getenv("VLLM_TARGET_DEVICE", "cuda"),

    # Maximum number of compilation jobs to run in parallel.
    # By default this is the number of CPUs
    "MAX_JOBS":
    lambda: os.getenv("MAX_JOBS", None),

    # Number of threads to use for nvcc
    # By default this is 1.
    # If set, `MAX_JOBS` will be reduced to avoid oversubscribing the CPU.
    "NVCC_THREADS":
    lambda: os.getenv("NVCC_THREADS", None),

    # If set, vllm will use precompiled binaries (*.so)
    "VLLM_USE_PRECOMPILED":
    lambda: bool(os.environ.get("VLLM_USE_PRECOMPILED")),

    # If set, vllm will install Punica kernels
    "VLLM_INSTALL_PUNICA_KERNELS":
    lambda: bool(int(os.getenv("VLLM_INSTALL_PUNICA_KERNELS", "0"))),

    # CMake build type
    # If not set, defaults to "Debug" or "RelWithDebInfo"
    # Available options: "Debug", "Release", "RelWithDebInfo"
    "CMAKE_BUILD_TYPE":
    lambda: os.getenv("CMAKE_BUILD_TYPE"),

    # If set, vllm will print verbose logs during installation
    "VERBOSE":
    lambda: bool(int(os.getenv('VERBOSE', '0'))),

    # Root directory for VLLM configuration files
    # Note that this not only affects how vllm finds its configuration files
    # during runtime, but also affects how vllm installs its configuration
    # files during **installation**.
    "VLLM_CONFIG_ROOT":
    lambda: os.environ.get("VLLM_CONFIG_ROOT", None) or os.getenv(
        "XDG_CONFIG_HOME", None) or os.path.expanduser("~/.config"),

    # ================== Runtime Env Vars ==================

    # used in distributed environment to determine the master address
    'VLLM_HOST_IP':
    lambda: os.getenv('VLLM_HOST_IP', "") or os.getenv("HOST_IP", ""),

    # used in distributed environment to manually set the communication port
    # Note: if VLLM_PORT is set, and some code asks for multiple ports, the
    # VLLM_PORT will be used as the first port, and the rest will be generated
    # by incrementing the VLLM_PORT value.
    # '0' is used to make mypy happy
    'VLLM_PORT':
    lambda: int(os.getenv('VLLM_PORT', '0'))
    if 'VLLM_PORT' in os.environ else None,

    # If true, will load models from ModelScope instead of Hugging Face Hub.
    # note that the value is true or false, not numbers
    "VLLM_USE_MODELSCOPE":
    lambda: os.environ.get("VLLM_USE_MODELSCOPE", "False").lower() == "true",

    # Instance id represents an instance of the VLLM. All processes in the same
    # instance should have the same instance id.
    "VLLM_INSTANCE_ID":
    lambda: os.environ.get("VLLM_INSTANCE_ID", None),

    # path to cudatoolkit home directory, under which should be bin, include,
    # and lib directories.
    "CUDA_HOME":
    lambda: os.environ.get("CUDA_HOME", None),

    # Path to the NCCL library file. It is needed because nccl>=2.19 brought
    # by PyTorch contains a bug: https://github.com/NVIDIA/nccl/issues/1234
    "VLLM_NCCL_SO_PATH":
    lambda: os.environ.get("VLLM_NCCL_SO_PATH", None),

    # when `VLLM_NCCL_SO_PATH` is not set, vllm will try to find the nccl
    # library file in the locations specified by `LD_LIBRARY_PATH`
    "LD_LIBRARY_PATH":
    lambda: os.environ.get("LD_LIBRARY_PATH", None),

    # flag to control if vllm should use triton flash attention
    "VLLM_USE_TRITON_FLASH_ATTN":
    lambda: (os.environ.get("VLLM_USE_TRITON_FLASH_ATTN", "True").lower() in
             ("true", "1")),

    # local rank of the process in the distributed setting, used to determine
    # the GPU device id
    "LOCAL_RANK":
    lambda: int(os.environ.get("LOCAL_RANK", "0")),

    # used to control the visible devices in the distributed setting
    "CUDA_VISIBLE_DEVICES":
    lambda: os.environ.get("CUDA_VISIBLE_DEVICES", None),

    # timeout for each iteration in the engine
    "VLLM_ENGINE_ITERATION_TIMEOUT_S":
    lambda: int(os.environ.get("VLLM_ENGINE_ITERATION_TIMEOUT_S", "60")),

    # API key for VLLM API server
    "VLLM_API_KEY":
    lambda: os.environ.get("VLLM_API_KEY", None),

    # S3 access information, used for tensorizer to load model from S3
    "S3_ACCESS_KEY_ID":
    lambda: os.environ.get("S3_ACCESS_KEY_ID", None),
    "S3_SECRET_ACCESS_KEY":
    lambda: os.environ.get("S3_SECRET_ACCESS_KEY", None),
    "S3_ENDPOINT_URL":
    lambda: os.environ.get("S3_ENDPOINT_URL", None),

    # Usage stats collection
    "VLLM_USAGE_STATS_SERVER":
    lambda: os.environ.get("VLLM_USAGE_STATS_SERVER", "https://stats.vllm.ai"),
    "VLLM_NO_USAGE_STATS":
    lambda: os.environ.get("VLLM_NO_USAGE_STATS", "0") == "1",
    "VLLM_DO_NOT_TRACK":
    lambda: (os.environ.get("VLLM_DO_NOT_TRACK", None) or os.environ.get(
        "DO_NOT_TRACK", None) or "0") == "1",
    "VLLM_USAGE_SOURCE":
    lambda: os.environ.get("VLLM_USAGE_SOURCE", "production"),

    # Logging configuration
    # If set to 0, vllm will not configure logging
    # If set to 1, vllm will configure logging using the default configuration
    #    or the configuration file specified by VLLM_LOGGING_CONFIG_PATH
    "VLLM_CONFIGURE_LOGGING":
    lambda: int(os.getenv("VLLM_CONFIGURE_LOGGING", "1")),
    "VLLM_LOGGING_CONFIG_PATH":
    lambda: os.getenv("VLLM_LOGGING_CONFIG_PATH"),

    # this is used for configuring the default logging level
    "VLLM_LOGGING_LEVEL":
    lambda: os.getenv("VLLM_LOGGING_LEVEL", "INFO"),

    # Trace function calls
    # If set to 1, vllm will trace function calls
    # Useful for debugging
    "VLLM_TRACE_FUNCTION":
    lambda: int(os.getenv("VLLM_TRACE_FUNCTION", "0")),

    # Backend for attention computation
    # Available options:
    # - "TORCH_SDPA": use torch.nn.MultiheadAttention
    # - "FLASH_ATTN": use FlashAttention
    # - "XFORMERS": use XFormers
    # - "ROCM_FLASH": use ROCmFlashAttention
    "VLLM_ATTENTION_BACKEND":
    lambda: os.getenv("VLLM_ATTENTION_BACKEND", None),

    # CPU key-value cache space
    # default is 4GB
    "VLLM_CPU_KVCACHE_SPACE":
    lambda: int(os.getenv("VLLM_CPU_KVCACHE_SPACE", "0")),

    # If the env var is set, it uses the Ray's compiled DAG API
    # which optimizes the control plane overhead.
    # Run vLLM with VLLM_USE_RAY_COMPILED_DAG=1 to enable it.
    "VLLM_USE_RAY_COMPILED_DAG":
    lambda: bool(os.getenv("VLLM_USE_RAY_COMPILED_DAG", 0)),

    # Use dedicated multiprocess context for workers.
    # Both spawn and fork work
    "VLLM_WORKER_MULTIPROC_METHOD":
    lambda: os.getenv("VLLM_WORKER_MULTIPROC_METHOD", "spawn"),

    # Timeout for fetching images when serving multimodal models
    # Default is 5 seconds
    "VLLM_IMAGE_FETCH_TIMEOUT":
    lambda: int(os.getenv("VLLM_IMAGE_FETCH_TIMEOUT", "5")),

    # Path to the XLA persistent cache directory.
    # Only used for XLA devices such as TPUs.
    "VLLM_XLA_CACHE_PATH":
    lambda: os.getenv("VLLM_XLA_CACHE_PATH", "~/.vllm/xla_cache/"),
}