环境变量¶
vLLM 使用以下环境变量来配置系统
警告
请注意，VLLM_PORT 和 VLLM_HOST_IP 设置了 vLLM 的端口和 IP，用于 内部使用。它们不是 API 服务器的端口和 IP。如果您使用 --host $VLLM_HOST_IP 和 --port $VLLM_PORT 来启动 API 服务器，将无法正常工作。
vLLM 使用的所有环境变量都以 VLLM_ 开头。Kubernetes 用户请特别注意：请勿将服务命名为 vllm，否则 Kubernetes 设置的环境变量可能会与 vLLM 的环境变量冲突，因为 Kubernetes 会为每个服务设置以大写服务名称为前缀的环境变量。
代码
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")) or bool(
os.environ.get("VLLM_PRECOMPILED_WHEEL_LOCATION")),
# Whether to force using nightly wheel in python build.
# This is used for testing the nightly wheel in python build.
"VLLM_TEST_USE_PRECOMPILED_NIGHTLY_WHEEL":
lambda: bool(int(os.getenv("VLLM_TEST_USE_PRECOMPILED_NIGHTLY_WHEEL", "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
# Defaults to `~/.config/vllm` unless `XDG_CONFIG_HOME` is set
# 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.path.expanduser(
os.getenv(
"VLLM_CONFIG_ROOT",
os.path.join(get_default_config_root(), "vllm"),
)),
# ================== Runtime Env Vars ==================
# Root directory for vLLM cache files
# Defaults to `~/.cache/vllm` unless `XDG_CACHE_HOME` is set
"VLLM_CACHE_ROOT":
lambda: os.path.expanduser(
os.getenv(
"VLLM_CACHE_ROOT",
os.path.join(get_default_cache_root(), "vllm"),
)),
# used in distributed environment to determine the ip address
# of the current node, when the node has multiple network interfaces.
# If you are using multi-node inference, you should set this differently
# on each node.
'VLLM_HOST_IP':
lambda: os.getenv('VLLM_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.
'VLLM_PORT':
get_vllm_port,
# path used for ipc when the frontend api server is running in
# multi-processing mode to communicate with the backend engine process.
'VLLM_RPC_BASE_PATH':
lambda: os.getenv('VLLM_RPC_BASE_PATH', tempfile.gettempdir()),
# 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",
# Interval in seconds to log a warning message when the ring buffer is full
"VLLM_RINGBUFFER_WARNING_INTERVAL":
lambda: int(os.environ.get("VLLM_RINGBUFFER_WARNING_INTERVAL", "60")),
# 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")),
# Use separate prefill and decode kernels for V1 attention instead of
# the unified triton kernel.
"VLLM_V1_USE_PREFILL_DECODE_ATTENTION":
lambda:
(os.getenv("VLLM_V1_USE_PREFILL_DECODE_ATTENTION", "False").lower() in
("true", "1")),
# Force vllm to use a specific flash-attention version (2 or 3), only valid
# when using the flash-attention backend.
"VLLM_FLASH_ATTN_VERSION":
lambda: maybe_convert_int(os.environ.get("VLLM_FLASH_ATTN_VERSION", None)),
# Internal flag to enable Dynamo fullgraph capture
"VLLM_TEST_DYNAMO_FULLGRAPH_CAPTURE":
lambda: bool(
os.environ.get("VLLM_TEST_DYNAMO_FULLGRAPH_CAPTURE", "1") != "0"),
# Feature flag to enable/disable Inductor standalone compile.
# In torch <= 2.7 we ignore this flag; in torch >= 2.8 this is
# enabled by default.
"VLLM_USE_STANDALONE_COMPILE":
lambda: os.environ.get("VLLM_USE_STANDALONE_COMPILE", "1") == "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),
# Whether to log responses from API Server for debugging
"VLLM_DEBUG_LOG_API_SERVER_RESPONSE":
lambda: os.environ.get("VLLM_DEBUG_LOG_API_SERVER_RESPONSE", "False"
).lower() == "true",
# 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").upper(),
# if set, VLLM_LOGGING_PREFIX will be prepended to all log messages
"VLLM_LOGGING_PREFIX":
lambda: os.getenv("VLLM_LOGGING_PREFIX", ""),
# if set, vllm will call logits processors in a thread pool with this many
# threads. This is useful when using custom logits processors that either
# (a) launch additional CUDA kernels or (b) do significant CPU-bound work
# while not holding the python GIL, or both.
"VLLM_LOGITS_PROCESSOR_THREADS":
lambda: int(os.getenv("VLLM_LOGITS_PROCESSOR_THREADS", "0"))
if "VLLM_LOGITS_PROCESSOR_THREADS" in os.environ else None,
# 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
# - "FLASHINFER": use flashinfer
# - "FLASHMLA": use FlashMLA
"VLLM_ATTENTION_BACKEND":
lambda: os.getenv("VLLM_ATTENTION_BACKEND", None),
# If set, vllm will use flashinfer sampler
"VLLM_USE_FLASHINFER_SAMPLER":
lambda: bool(int(os.environ["VLLM_USE_FLASHINFER_SAMPLER"]))
if "VLLM_USE_FLASHINFER_SAMPLER" in os.environ else None,
# If set, vllm will force flashinfer to use tensor cores;
# otherwise will use heuristic based on model architecture.
"VLLM_FLASHINFER_FORCE_TENSOR_CORES":
lambda: bool(int(os.getenv("VLLM_FLASHINFER_FORCE_TENSOR_CORES", "0"))),
# Pipeline stage partition strategy
"VLLM_PP_LAYER_PARTITION":
lambda: os.getenv("VLLM_PP_LAYER_PARTITION", None),
# (CPU backend only) CPU key-value cache space.
# default is None and will be set as 4 GB
"VLLM_CPU_KVCACHE_SPACE":
lambda: int(os.getenv("VLLM_CPU_KVCACHE_SPACE", "0"))
if "VLLM_CPU_KVCACHE_SPACE" in os.environ else None,
# (CPU backend only) CPU core ids bound by OpenMP threads, e.g., "0-31",
# "0,1,2", "0-31,33". CPU cores of different ranks are separated by '|'.
"VLLM_CPU_OMP_THREADS_BIND":
lambda: os.getenv("VLLM_CPU_OMP_THREADS_BIND", "auto"),
# (CPU backend only) CPU cores not used by OMP threads .
# Those CPU cores will not be used by OMP threads of a rank.
"VLLM_CPU_NUM_OF_RESERVED_CPU":
lambda: int(os.getenv("VLLM_CPU_NUM_OF_RESERVED_CPU", "0"))
if "VLLM_CPU_NUM_OF_RESERVED_CPU" in os.environ else None,
# (CPU backend only) whether to use prepack for MoE layer. This will be
# passed to ipex.llm.modules.GatedMLPMOE. On unsupported CPUs, you might
# need to set this to "0" (False).
"VLLM_CPU_MOE_PREPACK":
lambda: bool(int(os.getenv("VLLM_CPU_MOE_PREPACK", "1"))),
# (CPU backend only) whether to use SGL kernels, optimized for small batch.
"VLLM_CPU_SGL_KERNEL":
lambda: bool(int(os.getenv("VLLM_CPU_SGL_KERNEL", "0"))),
# If the env var is set, then all workers will execute as separate
# processes from the engine, and we use the same mechanism to trigger
# execution on all workers.
# Run vLLM with VLLM_USE_RAY_SPMD_WORKER=1 to enable it.
"VLLM_USE_RAY_SPMD_WORKER":
lambda: bool(int(os.getenv("VLLM_USE_RAY_SPMD_WORKER", "0"))),
# If the env var is set, it uses the Ray's Compiled Graph
# (previously known as ADAG) API which optimizes the
# control plane overhead.
# Run vLLM with VLLM_USE_RAY_COMPILED_DAG=1 to enable it.
# Note that this variable is set to 1 in V1 by default
# when ray distributed executor is used.
"VLLM_USE_RAY_COMPILED_DAG":
lambda: bool(int(os.getenv("VLLM_USE_RAY_COMPILED_DAG", "0"))),
# If the env var is set, Ray Compiled Graph uses the specified
# channel type to communicate between workers belonging to
# different pipeline-parallel stages.
# Available options:
# - "auto": use the default channel type
# - "nccl": use NCCL for communication
# - "shm": use shared memory and gRPC for communication
# This flag is ignored if VLLM_USE_RAY_COMPILED_DAG is not set.
"VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE":
lambda: os.getenv("VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE", "auto"),
# If the env var is set, it enables GPU communication overlap
# (experimental feature) in Ray's Compiled Graph. This flag is ignored if
# VLLM_USE_RAY_COMPILED_DAG is not set.
"VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM":
lambda: bool(int(os.getenv("VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM", "0"))
),
# Use dedicated multiprocess context for workers.
# Both spawn and fork work
"VLLM_WORKER_MULTIPROC_METHOD":
lambda: os.getenv("VLLM_WORKER_MULTIPROC_METHOD", "fork"),
# Path to the cache for storing downloaded assets
"VLLM_ASSETS_CACHE":
lambda: os.path.expanduser(
os.getenv(
"VLLM_ASSETS_CACHE",
os.path.join(get_default_cache_root(), "vllm", "assets"),
)),
# 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")),
# Timeout for fetching videos when serving multimodal models
# Default is 30 seconds
"VLLM_VIDEO_FETCH_TIMEOUT":
lambda: int(os.getenv("VLLM_VIDEO_FETCH_TIMEOUT", "30")),
# Timeout for fetching audio when serving multimodal models
# Default is 10 seconds
"VLLM_AUDIO_FETCH_TIMEOUT":
lambda: int(os.getenv("VLLM_AUDIO_FETCH_TIMEOUT", "10")),
# Maximum filesize in MB for a single audio file when processing
# speech-to-text requests. Files larger than this will be rejected.
# Default is 25 MB
"VLLM_MAX_AUDIO_CLIP_FILESIZE_MB":
lambda: int(os.getenv("VLLM_MAX_AUDIO_CLIP_FILESIZE_MB", "25")),
# Backend for Video IO
# - "opencv": Default backend that uses OpenCV stream buffered backend.
#
# Custom backend implementations can be registered
# via `@VIDEO_LOADER_REGISTRY.register("my_custom_video_loader")` and
# imported at runtime.
# If a non-existing backend is used, an AssertionError will be thrown.
"VLLM_VIDEO_LOADER_BACKEND":
lambda: os.getenv("VLLM_VIDEO_LOADER_BACKEND", "opencv"),
# Cache size (in GiB) for multimodal input cache
# Default is 4 GiB
"VLLM_MM_INPUT_CACHE_GIB":
lambda: int(os.getenv("VLLM_MM_INPUT_CACHE_GIB", "4")),
# Path to the XLA persistent cache directory.
# Only used for XLA devices such as TPUs.
"VLLM_XLA_CACHE_PATH":
lambda: os.path.expanduser(
os.getenv(
"VLLM_XLA_CACHE_PATH",
os.path.join(get_default_cache_root(), "vllm", "xla_cache"),
)),
# If set, assert on XLA recompilation after each execution step.
"VLLM_XLA_CHECK_RECOMPILATION":
lambda: bool(int(os.getenv("VLLM_XLA_CHECK_RECOMPILATION", "0"))),
# Enable SPMD mode for TPU backend.
"VLLM_XLA_USE_SPMD":
lambda: bool(int(os.getenv("VLLM_XLA_USE_SPMD", "0"))),
"VLLM_FUSED_MOE_CHUNK_SIZE":
lambda: int(os.getenv("VLLM_FUSED_MOE_CHUNK_SIZE", "32768")),
# Control whether to use fused MoE activation chunking. Current chunking
# logic is incompatible with torch.compile and causes IMA. See issue
# https://github.com/vllm-project/vllm/issues/19631.
"VLLM_ENABLE_FUSED_MOE_ACTIVATION_CHUNKING":
lambda: bool(
int(os.getenv("VLLM_ENABLE_FUSED_MOE_ACTIVATION_CHUNKING", "1"))),
# If set, vllm will skip the deprecation warnings.
"VLLM_NO_DEPRECATION_WARNING":
lambda: bool(int(os.getenv("VLLM_NO_DEPRECATION_WARNING", "0"))),
# 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)),
# If the env var VLLM_ALLOW_LONG_MAX_MODEL_LEN is set, it allows
# the user to specify a max sequence length greater than
# the max length derived from the model's config.json.
# To enable this, set VLLM_ALLOW_LONG_MAX_MODEL_LEN=1.
"VLLM_ALLOW_LONG_MAX_MODEL_LEN":
lambda:
(os.environ.get("VLLM_ALLOW_LONG_MAX_MODEL_LEN", "0").strip().lower() in
("1", "true")),
# If set, forces FP8 Marlin to be used for FP8 quantization regardless
# of the hardware support for FP8 compute.
"VLLM_TEST_FORCE_FP8_MARLIN":
lambda:
(os.environ.get("VLLM_TEST_FORCE_FP8_MARLIN", "0").strip().lower() in
("1", "true")),
"VLLM_TEST_FORCE_LOAD_FORMAT":
lambda: os.getenv("VLLM_TEST_FORCE_LOAD_FORMAT", "dummy"),
# Time in ms for the zmq client to wait for a response from the backend
# server for simple data operations
"VLLM_RPC_TIMEOUT":
lambda: int(os.getenv("VLLM_RPC_TIMEOUT", "10000")),
# Timeout in seconds for keeping HTTP connections alive in API server
"VLLM_HTTP_TIMEOUT_KEEP_ALIVE":
lambda: int(os.environ.get("VLLM_HTTP_TIMEOUT_KEEP_ALIVE", "5")),
# a list of plugin names to load, separated by commas.
# if this is not set, it means all plugins will be loaded
# if this is set to an empty string, no plugins will be loaded
"VLLM_PLUGINS":
lambda: None if "VLLM_PLUGINS" not in os.environ else os.environ[
"VLLM_PLUGINS"].split(","),
# a local directory to look in for unrecognized LoRA adapters.
# only works if plugins are enabled and
# VLLM_ALLOW_RUNTIME_LORA_UPDATING is enabled.
"VLLM_LORA_RESOLVER_CACHE_DIR":
lambda: os.getenv("VLLM_LORA_RESOLVER_CACHE_DIR", None),
# Enables torch profiler if set. Path to the directory where torch profiler
# traces are saved. Note that it must be an absolute path.
"VLLM_TORCH_PROFILER_DIR":
lambda: (None if os.getenv("VLLM_TORCH_PROFILER_DIR", None) is None else os
.path.expanduser(os.getenv("VLLM_TORCH_PROFILER_DIR", "."))),
# If set, vLLM will use Triton implementations of AWQ.
"VLLM_USE_TRITON_AWQ":
lambda: bool(int(os.getenv("VLLM_USE_TRITON_AWQ", "0"))),
# If set, allow loading or unloading lora adapters in runtime,
"VLLM_ALLOW_RUNTIME_LORA_UPDATING":
lambda:
(os.environ.get("VLLM_ALLOW_RUNTIME_LORA_UPDATING", "0").strip().lower() in
("1", "true")),
# By default, vLLM will check the peer-to-peer capability itself,
# in case of broken drivers. See https://github.com/vllm-project/vllm/blob/a9b15c606fea67a072416ea0ea115261a2756058/vllm/distributed/device_communicators/custom_all_reduce_utils.py#L101-L108 for details. # noqa
# If this env var is set to 1, vLLM will skip the peer-to-peer check,
# and trust the driver's peer-to-peer capability report.
"VLLM_SKIP_P2P_CHECK":
lambda: os.getenv("VLLM_SKIP_P2P_CHECK", "0") == "1",
# List of quantization kernels that should be disabled, used for testing
# and performance comparisons. Currently only affects MPLinearKernel
# selection
# (kernels: MacheteLinearKernel, MarlinLinearKernel, ExllamaLinearKernel)
"VLLM_DISABLED_KERNELS":
lambda: [] if "VLLM_DISABLED_KERNELS" not in os.environ else os.environ[
"VLLM_DISABLED_KERNELS"].split(","),
# If set, use the V1 code path.
"VLLM_USE_V1":
lambda: bool(int(os.getenv("VLLM_USE_V1", "1"))),
# Disable aiter ops unless specifically enabled.
# Acts as a parent switch to enable the rest of the other operations.
"VLLM_ROCM_USE_AITER":
lambda: (os.getenv("VLLM_ROCM_USE_AITER", "False").lower() in
("true", "1")),
# Whether to use aiter paged attention.
# By default is disabled.
"VLLM_ROCM_USE_AITER_PAGED_ATTN":
lambda: (os.getenv("VLLM_ROCM_USE_AITER_PAGED_ATTN", "False").lower() in
("true", "1")),
# use aiter linear op if aiter ops are enabled
# The following list of related ops
# - scaled_mm (per-tensor / rowwise)
"VLLM_ROCM_USE_AITER_LINEAR":
lambda: (os.getenv("VLLM_ROCM_USE_AITER_LINEAR", "True").lower() in
("true", "1")),
# Whether to use aiter moe ops.
# By default is enabled.
"VLLM_ROCM_USE_AITER_MOE":
lambda: (os.getenv("VLLM_ROCM_USE_AITER_MOE", "True").lower() in
("true", "1")),
# use aiter rms norm op if aiter ops are enabled.
"VLLM_ROCM_USE_AITER_RMSNORM":
lambda: (os.getenv("VLLM_ROCM_USE_AITER_RMSNORM", "True").lower() in
("true", "1")),
# Whether to use aiter mla ops.
# By default is enabled.
"VLLM_ROCM_USE_AITER_MLA":
lambda: (os.getenv("VLLM_ROCM_USE_AITER_MLA", "True").lower() in
("true", "1")),
# Whether to use aiter mha ops.
# By default is enabled.
"VLLM_ROCM_USE_AITER_MHA":
lambda: (os.getenv("VLLM_ROCM_USE_AITER_MHA", "True").lower() in
("true", "1")),
# use rocm skinny gemms
"VLLM_ROCM_USE_SKINNY_GEMM":
lambda: (os.getenv("VLLM_ROCM_USE_SKINNY_GEMM", "True").lower() in
("true", "1")),
# Pad the fp8 weights to 256 bytes for ROCm
"VLLM_ROCM_FP8_PADDING":
lambda: bool(int(os.getenv("VLLM_ROCM_FP8_PADDING", "1"))),
# Pad the weights for the moe kernel
"VLLM_ROCM_MOE_PADDING":
lambda: bool(int(os.getenv("VLLM_ROCM_MOE_PADDING", "1"))),
# custom paged attention kernel for MI3* cards
"VLLM_ROCM_CUSTOM_PAGED_ATTN":
lambda: (os.getenv("VLLM_ROCM_CUSTOM_PAGED_ATTN", "True").lower() in
("true", "1")),
# Custom quick allreduce kernel for MI3* cards
# Choice of quantization level: FP, INT8, INT6, INT4 or NONE
# Recommended for large models to get allreduce
"VLLM_ROCM_QUICK_REDUCE_QUANTIZATION":
lambda: os.getenv("VLLM_ROCM_QUICK_REDUCE_QUANTIZATION", "NONE").upper(),
# Custom quick allreduce kernel for MI3* cards
# Due to the lack of the bfloat16 asm instruction, bfloat16
# kernels are slower than fp16,
# If environment variable is set to 1, the input is converted to fp16
"VLLM_ROCM_QUICK_REDUCE_CAST_BF16_TO_FP16":
lambda:
(os.getenv("VLLM_ROCM_QUICK_REDUCE_CAST_BF16_TO_FP16", "True").lower() in
("true", "1")),
# Custom quick allreduce kernel for MI3* cards.
# Controls the maximum allowed number of data bytes(MB) for custom quick
# allreduce communication.
# Default: 2048 MB.
# Data exceeding this size will use either custom allreduce or RCCL
# communication.
"VLLM_ROCM_QUICK_REDUCE_MAX_SIZE_BYTES_MB":
lambda: maybe_convert_int(
os.environ.get("VLLM_ROCM_QUICK_REDUCE_MAX_SIZE_BYTES_MB", None)),
# Divisor for dynamic query scale factor calculation for FP8 KV Cache
"Q_SCALE_CONSTANT":
lambda: int(os.getenv("Q_SCALE_CONSTANT", "200")),
# Divisor for dynamic key scale factor calculation for FP8 KV Cache
"K_SCALE_CONSTANT":
lambda: int(os.getenv("K_SCALE_CONSTANT", "200")),
# Divisor for dynamic value scale factor calculation for FP8 KV Cache
"V_SCALE_CONSTANT":
lambda: int(os.getenv("V_SCALE_CONSTANT", "100")),
# If set, enable multiprocessing in LLM for the V1 code path.
"VLLM_ENABLE_V1_MULTIPROCESSING":
lambda: bool(int(os.getenv("VLLM_ENABLE_V1_MULTIPROCESSING", "1"))),
"VLLM_LOG_BATCHSIZE_INTERVAL":
lambda: float(os.getenv("VLLM_LOG_BATCHSIZE_INTERVAL", "-1")),
"VLLM_DISABLE_COMPILE_CACHE":
lambda: bool(int(os.getenv("VLLM_DISABLE_COMPILE_CACHE", "0"))),
# If set, vllm will run in development mode, which will enable
# some additional endpoints for developing and debugging,
# e.g. `/reset_prefix_cache`
"VLLM_SERVER_DEV_MODE":
lambda: bool(int(os.getenv("VLLM_SERVER_DEV_MODE", "0"))),
# Controls the maximum number of requests to handle in a
# single asyncio task when processing per-token outputs in the
# V1 AsyncLLM interface. It is applicable when handling a high
# concurrency of streaming requests.
# Setting this too high can result in a higher variance of
# inter-message latencies. Setting it too low can negatively impact
# TTFT and overall throughput.
"VLLM_V1_OUTPUT_PROC_CHUNK_SIZE":
lambda: int(os.getenv("VLLM_V1_OUTPUT_PROC_CHUNK_SIZE", "128")),
# If set, vLLM will disable the MLA attention optimizations.
"VLLM_MLA_DISABLE":
lambda: bool(int(os.getenv("VLLM_MLA_DISABLE", "0"))),
# Number of GPUs per worker in Ray, if it is set to be a fraction,
# it allows ray to schedule multiple actors on a single GPU,
# so that users can colocate other actors on the same GPUs as vLLM.
"VLLM_RAY_PER_WORKER_GPUS":
lambda: float(os.getenv("VLLM_RAY_PER_WORKER_GPUS", "1.0")),
# Bundle indices for Ray, if it is set, it can control precisely
# which indices are used for the Ray bundle, for every worker.
# Format: comma-separated list of integers, e.g. "0,1,2,3"
"VLLM_RAY_BUNDLE_INDICES":
lambda: os.getenv("VLLM_RAY_BUNDLE_INDICES", ""),
# In some system, find_loaded_library() may not work. So we allow users to
# specify the path through environment variable VLLM_CUDART_SO_PATH.
"VLLM_CUDART_SO_PATH":
lambda: os.getenv("VLLM_CUDART_SO_PATH", None),
# Rank of the process in the data parallel setting
"VLLM_DP_RANK":
lambda: int(os.getenv("VLLM_DP_RANK", "0")),
# Rank of the process in the data parallel setting.
# Defaults to VLLM_DP_RANK when not set.
"VLLM_DP_RANK_LOCAL":
lambda: int(
os.getenv("VLLM_DP_RANK_LOCAL", sys.modules[__name__].VLLM_DP_RANK)),
# World size of the data parallel setting
"VLLM_DP_SIZE":
lambda: int(os.getenv("VLLM_DP_SIZE", "1")),
# IP address of the master node in the data parallel setting
"VLLM_DP_MASTER_IP":
lambda: os.getenv("VLLM_DP_MASTER_IP", "127.0.0.1"),
# Port of the master node in the data parallel setting
"VLLM_DP_MASTER_PORT":
lambda: int(os.getenv("VLLM_DP_MASTER_PORT", "0")),
# In the context of executing MoE models with Data-Parallel, Expert-Parallel
# and Batched All-to-All dispatch/combine kernels, VLLM_MOE_DP_CHUNK_SIZE
# dictates the quantum of tokens that can be dispatched from a DP
# rank. All DP ranks process the activations in VLLM_MOE_DP_CHUNK_SIZE
# units.
"VLLM_MOE_DP_CHUNK_SIZE":
lambda: int(os.getenv("VLLM_MOE_DP_CHUNK_SIZE", "256")),
# Randomize inputs during dummy runs when using Data Parallel
"VLLM_RANDOMIZE_DP_DUMMY_INPUTS":
lambda: os.environ.get("VLLM_RANDOMIZE_DP_DUMMY_INPUTS", "0") == "1",
# Whether to use S3 path for model loading in CI via RunAI Streamer
"VLLM_CI_USE_S3":
lambda: os.environ.get("VLLM_CI_USE_S3", "0") == "1",
# Use model_redirect to redirect the model name to a local folder.
# `model_redirect` can be a json file mapping the model between
# repo_id and local folder:
# {"meta-llama/Llama-3.2-1B": "/tmp/Llama-3.2-1B"}
# or a space separated values table file:
# meta-llama/Llama-3.2-1B /tmp/Llama-3.2-1B
"VLLM_MODEL_REDIRECT_PATH":
lambda: os.environ.get("VLLM_MODEL_REDIRECT_PATH", None),
# Whether to use atomicAdd reduce in gptq/awq marlin kernel.
"VLLM_MARLIN_USE_ATOMIC_ADD":
lambda: os.environ.get("VLLM_MARLIN_USE_ATOMIC_ADD", "0") == "1",
# Whether to turn on the outlines cache for V0
# This cache is unbounded and on disk, so it's not safe to use in
# an environment with potentially malicious users.
"VLLM_V0_USE_OUTLINES_CACHE":
lambda: os.environ.get("VLLM_V0_USE_OUTLINES_CACHE", "0") == "1",
# Whether to turn on the outlines cache for V1
# This cache is unbounded and on disk, so it's not safe to use in
# an environment with potentially malicious users.
"VLLM_V1_USE_OUTLINES_CACHE":
lambda: os.environ.get("VLLM_V1_USE_OUTLINES_CACHE", "0") == "1",
# Gap between padding buckets for the forward pass. So we have
# 8, we will run forward pass with [16, 24, 32, ...].
"VLLM_TPU_BUCKET_PADDING_GAP":
lambda: int(os.environ["VLLM_TPU_BUCKET_PADDING_GAP"])
if "VLLM_TPU_BUCKET_PADDING_GAP" in os.environ else 0,
"VLLM_TPU_MOST_MODEL_LEN":
lambda: maybe_convert_int(os.environ.get("VLLM_TPU_MOST_MODEL_LEN", None)),
# Allow use of DeepGemm kernels for fused moe ops.
"VLLM_USE_DEEP_GEMM":
lambda: bool(int(os.getenv("VLLM_USE_DEEP_GEMM", "0"))),
# Allow use of FlashInfer MoE kernels for fused moe ops.
"VLLM_USE_FLASHINFER_MOE_FP8":
lambda: bool(int(os.getenv("VLLM_USE_FLASHINFER_MOE_FP8", "0"))),
# Allow use of FlashInfer CUTLASS kernels for fused moe ops.
"VLLM_USE_FLASHINFER_MOE_FP4":
lambda: bool(int(os.getenv("VLLM_USE_FLASHINFER_MOE_FP4", "0"))),
# Control the cache sized used by the xgrammar compiler. The default
# of 512 MB should be enough for roughly 1000 JSON schemas.
# It can be changed with this variable if needed for some reason.
"VLLM_XGRAMMAR_CACHE_MB":
lambda: int(os.getenv("VLLM_XGRAMMAR_CACHE_MB", "512")),
# Control the threshold for msgspec to use 'zero copy' for
# serialization/deserialization of tensors. Tensors below
# this limit will be encoded into the msgpack buffer, and
# tensors above will instead be sent via a separate message.
# While the sending side still actually copies the tensor
# in all cases, on the receiving side, tensors above this
# limit will actually be zero-copy decoded.
"VLLM_MSGPACK_ZERO_COPY_THRESHOLD":
lambda: int(os.getenv("VLLM_MSGPACK_ZERO_COPY_THRESHOLD", "256")),
# If set, allow insecure serialization using pickle.
# This is useful for environments where it is deemed safe to use the
# insecure method and it is needed for some reason.
"VLLM_ALLOW_INSECURE_SERIALIZATION":
lambda: bool(int(os.getenv("VLLM_ALLOW_INSECURE_SERIALIZATION", "0"))),
# IP address used for NIXL handshake between remote agents.
"VLLM_NIXL_SIDE_CHANNEL_HOST":
lambda: os.getenv("VLLM_NIXL_SIDE_CHANNEL_HOST", "localhost"),
# Port used for NIXL handshake between remote agents.
"VLLM_NIXL_SIDE_CHANNEL_PORT":
lambda: int(os.getenv("VLLM_NIXL_SIDE_CHANNEL_PORT", "5557")),
# all2all backend for vllm's expert parallel communication
# Available options:
# - "naive": naive all2all implementation using all-reduce
# - "pplx": use pplx kernels
# - "deepep_high_throughput", use deepep high-throughput kernels
# - "deepep_low_latency", use deepep low-latency kernels
"VLLM_ALL2ALL_BACKEND":
lambda: os.getenv("VLLM_ALL2ALL_BACKEND", "naive"),
# Control the maximum number of tokens per expert supported by the
# NVFP4 MoE CUTLASS Kernel. This value is used to create a buffer for
# the blockscale tensor of activations NVFP4 Quantization.
# This is used to prevent the kernel from running out of memory.
"VLLM_MAX_TOKENS_PER_EXPERT_FP4_MOE":
lambda: int(os.getenv("VLLM_MAX_TOKENS_PER_EXPERT_FP4_MOE", "163840")),
# Regex timeout for use by the vLLM tool parsing plugins.
"VLLM_TOOL_PARSE_REGEX_TIMEOUT_SECONDS":
lambda: int(os.getenv("VLLM_TOOL_PARSE_REGEX_TIMEOUT_SECONDS", "1")),
# Reduce CPU usage when vLLM is idle. Enabling this will incur small
# latency penalty when a request eventually comes.
"VLLM_SLEEP_WHEN_IDLE":
lambda: bool(int(os.getenv("VLLM_SLEEP_WHEN_IDLE", "0"))),
# Control the max chunk bytes (in MB) for the rpc message queue.
# Object larger than this threshold will be broadcast to worker
# processes via zmq.
"VLLM_MQ_MAX_CHUNK_BYTES_MB":
lambda: int(os.getenv("VLLM_MQ_MAX_CHUNK_BYTES_MB", "16")),
# Timeout in seconds for execute_model RPC calls in multiprocessing
# executor (only applies when TP > 1).
"VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS":
lambda: int(os.getenv("VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS", "300")),
# KV Cache layout used throughout vllm.
# Some common values are:
# - NHD
# - HND
# Where N=num_blocks, H=num_heads and D=head_size. The default value will
# leave the layout choice to the backend. Mind that backends may only
# implement and support a subset of all possible layouts.
"VLLM_KV_CACHE_LAYOUT":
lambda: os.getenv("VLLM_KV_CACHE_LAYOUT", None),
# Enable checking whether the generated logits contain NaNs,
# indicating corrupted output. Useful for debugging low level bugs
# or bad hardware but it may add compute overhead.
"VLLM_COMPUTE_NANS_IN_LOGITS":
lambda: bool(int(os.getenv("VLLM_COMPUTE_NANS_IN_LOGITS", "0"))),
# Controls whether or not emulations are used for NVFP4
# generations on machines < 100 for compressed-tensors
# models
"VLLM_USE_NVFP4_CT_EMULATIONS":
lambda: bool(int(os.getenv("VLLM_USE_NVFP4_CT_EMULATIONS", "0"))),
# Time (in seconds) after which the KV cache on the producer side is
# automatically cleared if no READ notification is received from the
# consumer. This is only applicable when using NixlConnector in a
# disaggregated decode-prefill setup.
"VLLM_NIXL_ABORT_REQUEST_TIMEOUT":
lambda: int(os.getenv("VLLM_NIXL_ABORT_REQUEST_TIMEOUT", "120")),
# Controls whether or not to use cudnn prefill
"VLLM_USE_CUDNN_PREFILL":
lambda: bool(int(os.getenv("VLLM_USE_CUDNN_PREFILL", "0"))),
# If set to 1, use the TRTLLM Decode Attention backend in flashinfer.
"VLLM_USE_TRTLLM_DECODE_ATTENTION":
lambda: os.getenv("VLLM_USE_TRTLLM_DECODE_ATTENTION", None),
# Controls garbage collection during CUDA graph capture.
# If set to 0 (default), enables GC freezing to speed up capture time.
# If set to 1, allows GC to run during capture.
"VLLM_ENABLE_CUDAGRAPH_GC":
lambda: bool(int(os.getenv("VLLM_ENABLE_CUDAGRAPH_GC", "0"))),
# Used to force set up loopback IP
"VLLM_LOOPBACK_IP":
lambda: os.getenv("VLLM_LOOPBACK_IP", ""),
# Used to set the process name prefix for vLLM processes.
# This is useful for debugging and monitoring purposes.
# The default value is "VLLM".
"VLLM_PROCESS_NAME_PREFIX":
lambda: os.getenv("VLLM_PROCESS_NAME_PREFIX", "VLLM"),
}