Environment variables

Environment variables configure your vLLM Worker by providing control over model selection, access credentials, and operational parameters necessary for optimal Worker performance.

CUDA versions

Operating your vLLM Worker with different CUDA versions enhances compatibility and performance across various hardware configurations. When deploying, ensure you choose an appropriate CUDA version based on your needs.

CUDA Version	Stable Image Tag	Development Image Tag	Note
12.1.0	runpod/worker-v1-vllm:stable-cuda12.1.0	runpod/worker-v1-vllm:dev-cuda12.1.0	When creating an Endpoint, select CUDA Version 12.2 and 12.1 in the GPU filter.

This table provides a reference to the image tags you should use based on the desired CUDA version and image stability, stable or development. Ensure you follow the selection note for CUDA 12.1.0 compatibility.

Environment variables

note

0 is equivalent to False and 1 is equivalent to True for boolean values.

Name	Default	Type/Choices	Description
MODEL_NAME	'facebook/opt-125m'	str	Name or path of the Hugging Face model to use.
TOKENIZER	None	str	Name or path of the Hugging Face tokenizer to use.
SKIP_TOKENIZER_INIT	False	bool	Skip initialization of tokenizer and detokenizer.
TOKENIZER_MODE	'auto'	['auto', 'slow']	The tokenizer mode.
TRUST_REMOTE_CODE	False	bool	Trust remote code from Hugging Face.
DOWNLOAD_DIR	None	str	Directory to download and load the weights.
LOAD_FORMAT	'auto'	str	The format of the model weights to load.
HF_TOKEN	-	str	Hugging Face token for private and gated models.
DTYPE	'auto'	['auto', 'half', 'float16', 'bfloat16', 'float', 'float32']	Data type for model weights and activations.
KV_CACHE_DTYPE	'auto'	['auto', 'fp8']	Data type for KV cache storage.
QUANTIZATION_PARAM_PATH	None	str	Path to the JSON file containing the KV cache scaling factors.
MAX_MODEL_LEN	None	int	Model context length.
GUIDED_DECODING_BACKEND	'outlines'	['outlines', 'lm-format-enforcer']	Which engine will be used for guided decoding by default.
DISTRIBUTED_EXECUTOR_BACKEND	None	['ray', 'mp']	Backend to use for distributed serving.
WORKER_USE_RAY	False	bool	Deprecated, use --distributed-executor-backend=ray.
PIPELINE_PARALLEL_SIZE	1	int	Number of pipeline stages.
TENSOR_PARALLEL_SIZE	1	int	Number of tensor parallel replicas.
MAX_PARALLEL_LOADING_WORKERS	None	int	Load model sequentially in multiple batches.
RAY_WORKERS_USE_NSIGHT	False	bool	If specified, use nsight to profile Ray workers.
ENABLE_PREFIX_CACHING	False	bool	Enables automatic prefix caching.
DISABLE_SLIDING_WINDOW	False	bool	Disables sliding window, capping to sliding window size.
USE_V2_BLOCK_MANAGER	False	bool	Use BlockSpaceMangerV2.
NUM_LOOKAHEAD_SLOTS	0	int	Experimental scheduling config necessary for speculative decoding.
SEED	0	int	Random seed for operations.
NUM_GPU_BLOCKS_OVERRIDE	None	int	If specified, ignore GPU profiling result and use this number of GPU blocks.
MAX_NUM_BATCHED_TOKENS	None	int	Maximum number of batched tokens per iteration.
MAX_NUM_SEQS	256	int	Maximum number of sequences per iteration.
MAX_LOGPROBS	20	int	Max number of log probs to return when logprobs is specified in SamplingParams.
DISABLE_LOG_STATS	False	bool	Disable logging statistics.
QUANTIZATION	None	['awq', 'squeezellm', 'gptq']	Method used to quantize the weights.
ROPE_SCALING	None	dict	RoPE scaling configuration in JSON format.
ROPE_THETA	None	float	RoPE theta. Use with rope_scaling.
TOKENIZER_POOL_SIZE	0	int	Size of tokenizer pool to use for asynchronous tokenization.
TOKENIZER_POOL_TYPE	'ray'	str	Type of tokenizer pool to use for asynchronous tokenization.
TOKENIZER_POOL_EXTRA_CONFIG	None	dict	Extra config for tokenizer pool.
ENABLE_LORA	False	bool	If True, enable handling of LoRA adapters.
MAX_LORAS	1	int	Max number of LoRAs in a single batch.
MAX_LORA_RANK	16	int	Max LoRA rank.
LORA_EXTRA_VOCAB_SIZE	256	int	Maximum size of extra vocabulary for LoRA adapters.
LORA_DTYPE	'auto'	['auto', 'float16', 'bfloat16', 'float32']	Data type for LoRA.
LONG_LORA_SCALING_FACTORS	None	tuple	Specify multiple scaling factors for LoRA adapters.
MAX_CPU_LORAS	None	int	Maximum number of LoRAs to store in CPU memory.
FULLY_SHARDED_LORAS	False	bool	Enable fully sharded LoRA layers.
SCHEDULER_DELAY_FACTOR	0.0	float	Apply a delay before scheduling next prompt.
ENABLE_CHUNKED_PREFILL	False	bool	Enable chunked prefill requests.
SPECULATIVE_MODEL	None	str	The name of the draft model to be used in speculative decoding.
NUM_SPECULATIVE_TOKENS	None	int	The number of speculative tokens to sample from the draft model.
SPECULATIVE_DRAFT_TENSOR_PARALLEL_SIZE	None	int	Number of tensor parallel replicas for the draft model.
SPECULATIVE_MAX_MODEL_LEN	None	int	The maximum sequence length supported by the draft model.
SPECULATIVE_DISABLE_BY_BATCH_SIZE	None	int	Disable speculative decoding if the number of enqueue requests is larger than this value.
NGRAM_PROMPT_LOOKUP_MAX	None	int	Max size of window for ngram prompt lookup in speculative decoding.
NGRAM_PROMPT_LOOKUP_MIN	None	int	Min size of window for ngram prompt lookup in speculative decoding.
SPEC_DECODING_ACCEPTANCE_METHOD	'rejection_sampler'	['rejection_sampler', 'typical_acceptance_sampler']	Specify the acceptance method for draft token verification in speculative decoding.
TYPICAL_ACCEPTANCE_SAMPLER_POSTERIOR_THRESHOLD	None	float	Set the lower bound threshold for the posterior probability of a token to be accepted.
TYPICAL_ACCEPTANCE_SAMPLER_POSTERIOR_ALPHA	None	float	A scaling factor for the entropy-based threshold for token acceptance.
MODEL_LOADER_EXTRA_CONFIG	None	dict	Extra config for model loader.
PREEMPTION_MODE	None	str	If 'recompute', the engine performs preemption-aware recomputation. If 'save', the engine saves activations into the CPU memory as preemption happens.
PREEMPTION_CHECK_PERIOD	1.0	float	How frequently the engine checks if a preemption happens.
PREEMPTION_CPU_CAPACITY	2	float	The percentage of CPU memory used for the saved activations.
DISABLE_LOGGING_REQUEST	False	bool	Disable logging requests.
MAX_LOG_LEN	None	int	Max number of prompt characters or prompt ID numbers being printed in log.
Tokenizer Settings
TOKENIZER_NAME	None	str	Tokenizer repository to use a different tokenizer than the model's default.
TOKENIZER_REVISION	None	str	Tokenizer revision to load.
CUSTOM_CHAT_TEMPLATE	None	str of single-line jinja template	Custom chat jinja template. More Info
System, GPU, and Tensor Parallelism(Multi-GPU) Settings
GPU_MEMORY_UTILIZATION	0.95	float	Sets GPU VRAM utilization.
MAX_PARALLEL_LOADING_WORKERS	None	int	Load model sequentially in multiple batches, to avoid RAM OOM when using tensor parallel and large models.
BLOCK_SIZE	16	8, 16, 32	Token block size for contiguous chunks of tokens.
SWAP_SPACE	4	int	CPU swap space size (GiB) per GPU.
ENFORCE_EAGER	False	bool	Always use eager-mode PyTorch. If False(0), will use eager mode and CUDA graph in hybrid for maximal performance and flexibility.
MAX_SEQ_LEN_TO_CAPTURE	8192	int	Maximum context length covered by CUDA graphs. When a sequence has context length larger than this, we fall back to eager mode.
DISABLE_CUSTOM_ALL_REDUCE	0	int	Enables or disables custom all reduce.
Streaming Batch Size Settings:
DEFAULT_BATCH_SIZE	50	int	Default and Maximum batch size for token streaming to reduce HTTP calls.
DEFAULT_MIN_BATCH_SIZE	1	int	Batch size for the first request, which will be multiplied by the growth factor every subsequent request.
DEFAULT_BATCH_SIZE_GROWTH_FACTOR	3	float	Growth factor for dynamic batch size.
The way this works is that the first request will have a batch size of DEFAULT_MIN_BATCH_SIZE, and each subsequent request will have a batch size of previous_batch_size * DEFAULT_BATCH_SIZE_GROWTH_FACTOR. This will continue until the batch size reaches DEFAULT_BATCH_SIZE. E.g. for the default values, the batch sizes will be 1, 3, 9, 27, 50, 50, 50, .... You can also specify this per request, with inputs max_batch_size, min_batch_size, and batch_size_growth_factor. This has nothing to do with vLLM's internal batching, but rather the number of tokens sent in each HTTP request from the worker
OpenAI Settings
RAW_OPENAI_OUTPUT	1	boolean as int	Enables raw OpenAI SSE format string output when streaming. Required to be enabled (which it is by default) for OpenAI compatibility.
OPENAI_SERVED_MODEL_NAME_OVERRIDE	None	str	Overrides the name of the served model from model repo/path to specified name, which you will then be able to use the value for the model parameter when making OpenAI requests
OPENAI_RESPONSE_ROLE	assistant	str	Role of the LLM's Response in OpenAI Chat Completions.
Serverless Settings
MAX_CONCURRENCY	300	int	Max concurrent requests per worker. vLLM has an internal queue, so you don't have to worry about limiting by VRAM, this is for improving scaling/load balancing efficiency
DISABLE_LOG_STATS	False	bool	Enables or disables vLLM stats logging.
DISABLE_LOG_REQUESTS	False	bool	Enables or disables vLLM request logging.

note

If you are facing issues when using Mixtral 8x7B, Quantized models, or handling unusual models/architectures, try setting TRUST_REMOTE_CODE to 1.