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Engine Arguments

Engine arguments control the behavior of the vLLM engine.

The engine argument classes, EngineArgs and AsyncEngineArgs, are a combination of the configuration classes defined in vllm.config. Therefore, if you are interested in developer documentation, we recommend looking at these configuration classes as they are the source of truth for types, defaults and docstrings.

EngineArgs

--use-v2-block-manager

[DEPRECATED] block manager v1 has been removed and SelfAttnBlockSpaceManager (i.e. block manager v2) is now the default. Setting this flag to True or False has no effect on vLLM behavior.

Default: True

--disable-log-stats

Disable logging statistics.

Default: False

ModelConfig

Configuration for the model.

--model

Name or path of the Hugging Face model to use. It is also used as the content for model_name tag in metrics output when served_model_name is not specified.

Default: Qwen/Qwen3-0.6B

--task

Possible choices: auto, classify, draft, embed, embedding, generate, reward, score, transcription

The task to use the model for. If the model supports more than one model runner, this is used to select which model runner to run.

Note that the model may support other tasks using the same model runner.

Default: auto

--tokenizer

Name or path of the Hugging Face tokenizer to use. If unspecified, model name or path will be used.

Default: None

--tokenizer-mode

Possible choices: auto, custom, mistral, slow

Tokenizer mode:

  • "auto" will use the fast tokenizer if available.

  • "slow" will always use the slow tokenizer.

  • "mistral" will always use the tokenizer from mistral_common.

  • "custom" will use --tokenizer to select the preregistered tokenizer.

Default: auto

--trust-remote-code, --no-trust-remote-code

Trust remote code (e.g., from HuggingFace) when downloading the model and tokenizer.

Default: False

--dtype

Possible choices: auto, bfloat16, float, float16, float32, half

Data type for model weights and activations:

  • "auto" will use FP16 precision for FP32 and FP16 models, and BF16 precision for BF16 models.

  • "half" for FP16. Recommended for AWQ quantization.

  • "float16" is the same as "half".

  • "bfloat16" for a balance between precision and range.

  • "float" is shorthand for FP32 precision.

  • "float32" for FP32 precision.

Default: auto

--seed

Random seed for reproducibility. Initialized to None in V0, but initialized to 0 in V1.

Default: None

--hf-config-path

Name or path of the Hugging Face config to use. If unspecified, model name or path will be used.

Default: None

--allowed-local-media-path

Allowing API requests to read local images or videos from directories specified by the server file system. This is a security risk. Should only be enabled in trusted environments.

Default: ``

--revision

The specific model version to use. It can be a branch name, a tag name, or a commit id. If unspecified, will use the default version.

Default: None

--code-revision

The specific revision to use for the model code on the Hugging Face Hub. It can be a branch name, a tag name, or a commit id. If unspecified, will use the default version.

Default: None

--rope-scaling

RoPE scaling configuration. For example, {"rope_type":"dynamic","factor":2.0}.

Should either be a valid JSON string or JSON keys passed individually. For example, the following sets of arguments are equivalent:

  • --json-arg '{"key1": "value1", "key2": {"key3": "value2"}}'

  • --json-arg.key1 value1 --json-arg.key2.key3 value2

Additionally, list elements can be passed individually using +:

  • --json-arg '{"key4": ["value3", "value4", "value5"]}'

  • --json-arg.key4+ value3 --json-arg.key4+='value4,value5'

Default: {}

--rope-theta

RoPE theta. Use with rope_scaling. In some cases, changing the RoPE theta improves the performance of the scaled model.

Default: None

--tokenizer-revision

The specific revision to use for the tokenizer on the Hugging Face Hub. It can be a branch name, a tag name, or a commit id. If unspecified, will use the default version.

Default: None

--max-model-len

Model context length (prompt and output). If unspecified, will be automatically derived from the model config.

When passing via --max-model-len, supports k/m/g/K/M/G in human-readable format. Examples:

  • 1k -> 1000

  • 1K -> 1024

  • 25.6k -> 25,600

Default: None

--quantization, -q

Method used to quantize the weights. If None, we first check the quantization_config attribute in the model config file. If that is None, we assume the model weights are not quantized and use dtype to determine the data type of the weights.

Default: None

--enforce-eager, --no-enforce-eager

Whether to always use eager-mode PyTorch. If True, we will disable CUDA graph and always execute the model in eager mode. If False, we will use CUDA graph and eager execution in hybrid for maximal performance and flexibility.

Default: False

--max-seq-len-to-capture

Maximum sequence len covered by CUDA graphs. When a sequence has context length larger than this, we fall back to eager mode. Additionally for encoder-decoder models, if the sequence length of the encoder input is larger than this, we fall back to the eager mode.

Default: 8192

--max-logprobs

Maximum number of log probabilities to return when logprobs is specified in SamplingParams. The default value comes the default for the OpenAI Chat Completions API.

Default: 20

--disable-sliding-window, --no-disable-sliding-window

Whether to disable sliding window. If True, we will disable the sliding window functionality of the model, capping to sliding window size. If the model does not support sliding window, this argument is ignored.

Default: False

--disable-cascade-attn, --no-disable-cascade-attn

Disable cascade attention for V1. While cascade attention does not change the mathematical correctness, disabling it could be useful for preventing potential numerical issues. Note that even if this is set to False, cascade attention will be only used when the heuristic tells that it's beneficial.

Default: False

--skip-tokenizer-init, --no-skip-tokenizer-init

Skip initialization of tokenizer and detokenizer. Expects valid prompt_token_ids and None for prompt from the input. The generated output will contain token ids.

Default: False

--enable-prompt-embeds, --no-enable-prompt-embeds

If True, enables passing text embeddings as inputs via the prompt_embeds key. Note that enabling this will double the time required for graph compilation.

Default: False

--served-model-name

The model name(s) used in the API. If multiple names are provided, the server will respond to any of the provided names. The model name in the model field of a response will be the first name in this list. If not specified, the model name will be the same as the --model argument. Noted that this name(s) will also be used in model_name tag content of prometheus metrics, if multiple names provided, metrics tag will take the first one.

Default: None

--disable-async-output-proc

Disable async output processing. This may result in lower performance.

Default: False

--config-format

Possible choices: auto, hf, mistral

The format of the model config to load:

  • "auto" will try to load the config in hf format if available else it will try to load in mistral format.

  • "hf" will load the config in hf format.

  • "mistral" will load the config in mistral format.

Default: auto

--hf-token

The token to use as HTTP bearer authorization for remote files . If True, will use the token generated when running huggingface-cli login (stored in ~/.huggingface).

Default: None

--hf-overrides

If a dictionary, contains arguments to be forwarded to the Hugging Face config. If a callable, it is called to update the HuggingFace config.

Default: {}

--override-neuron-config

Initialize non-default neuron config or override default neuron config that are specific to Neuron devices, this argument will be used to configure the neuron config that can not be gathered from the vllm arguments. e.g. {"cast_logits_dtype": "bfloat16"}.

Should either be a valid JSON string or JSON keys passed individually. For example, the following sets of arguments are equivalent:

  • --json-arg '{"key1": "value1", "key2": {"key3": "value2"}}'

  • --json-arg.key1 value1 --json-arg.key2.key3 value2

Additionally, list elements can be passed individually using +:

  • --json-arg '{"key4": ["value3", "value4", "value5"]}'

  • --json-arg.key4+ value3 --json-arg.key4+='value4,value5'

Default: {}

--override-pooler-config

Initialize non-default pooling config or override default pooling config for the pooling model. e.g. {"pooling_type": "mean", "normalize": false}.

Default: None

--logits-processor-pattern

Optional regex pattern specifying valid logits processor qualified names that can be passed with the logits_processors extra completion argument. Defaults to None, which allows no processors.

Default: None

--generation-config

The folder path to the generation config. Defaults to "auto", the generation config will be loaded from model path. If set to "vllm", no generation config is loaded, vLLM defaults will be used. If set to a folder path, the generation config will be loaded from the specified folder path. If max_new_tokens is specified in generation config, then it sets a server-wide limit on the number of output tokens for all requests.

Default: auto

--override-generation-config

Overrides or sets generation config. e.g. {"temperature": 0.5}. If used with --generation-config auto, the override parameters will be merged with the default config from the model. If used with --generation-config vllm, only the override parameters are used.

Should either be a valid JSON string or JSON keys passed individually. For example, the following sets of arguments are equivalent:

  • --json-arg '{"key1": "value1", "key2": {"key3": "value2"}}'

  • --json-arg.key1 value1 --json-arg.key2.key3 value2

Additionally, list elements can be passed individually using +:

  • --json-arg '{"key4": ["value3", "value4", "value5"]}'

  • --json-arg.key4+ value3 --json-arg.key4+='value4,value5'

Default: {}

--enable-sleep-mode, --no-enable-sleep-mode

Enable sleep mode for the engine (only cuda platform is supported).

Default: False

--model-impl

Possible choices: auto, vllm, transformers

Which implementation of the model to use:

  • "auto" will try to use the vLLM implementation, if it exists, and fall back to the Transformers implementation if no vLLM implementation is available.

  • "vllm" will use the vLLM model implementation.

  • "transformers" will use the Transformers model implementation.

Default: auto

--override-attention-dtype

Override dtype for attention

Default: None

LoadConfig

Configuration for loading the model weights.

--load-format

Possible choices: auto, pt, safetensors, npcache, dummy, tensorizer, sharded_state, gguf, bitsandbytes, mistral, runai_streamer, runai_streamer_sharded, fastsafetensors

The format of the model weights to load:

  • "auto" will try to load the weights in the safetensors format and fall back to the pytorch bin format if safetensors format is not available.

  • "pt" will load the weights in the pytorch bin format.

  • "safetensors" will load the weights in the safetensors format.

  • "npcache" will load the weights in pytorch format and store a numpy cache to speed up the loading.

  • "dummy" will initialize the weights with random values, which is mainly for profiling.

  • "tensorizer" will use CoreWeave's tensorizer library for fast weight loading. See the Tensorize vLLM Model script in the Examples section for more information.

  • "runai_streamer" will load the Safetensors weights using Run:ai Model Streamer.

  • "bitsandbytes" will load the weights using bitsandbytes quantization.

  • "sharded_state" will load weights from pre-sharded checkpoint files, supporting efficient loading of tensor-parallel models.

  • "gguf" will load weights from GGUF format files (details specified in https://github.com/ggml-org/ggml/blob/master/docs/gguf.md).

  • "mistral" will load weights from consolidated safetensors files used by Mistral models.

Default: auto

--download-dir

Directory to download and load the weights, default to the default cache directory of Hugging Face.

Default: None

--model-loader-extra-config

Extra config for model loader. This will be passed to the model loader corresponding to the chosen load_format.

Default: {}

--ignore-patterns

The list of patterns to ignore when loading the model. Default to "original/*/" to avoid repeated loading of llama's checkpoints.

Default: None

--use-tqdm-on-load, --no-use-tqdm-on-load

Whether to enable tqdm for showing progress bar when loading model weights.

Default: True

--pt-load-map-location

pt_load_map_location: the map location for loading pytorch checkpoint, to support loading checkpoints can only be loaded on certain devices like "cuda", this is equivalent to {"": "cuda"}. Another supported format is mapping from different devices like from GPU 1 to GPU 0: {"cuda:1": "cuda:0"}. Note that when passed from command line, the strings in dictionary needs to be double quoted for json parsing. For more details, see original doc for map_location in https://pytorch.org/docs/stable/generated/torch.load.html

Default: cpu

DecodingConfig

Dataclass which contains the decoding strategy of the engine.

--guided-decoding-backend

Possible choices: auto, guidance, lm-format-enforcer, outlines, xgrammar

Which engine will be used for guided decoding (JSON schema / regex etc) by default. With "auto", we will make opinionated choices based on request contents and what the backend libraries currently support, so the behavior is subject to change in each release.

Default: auto

--guided-decoding-disable-fallback, --no-guided-decoding-disable-fallback

If True, vLLM will not fallback to a different backend on error.

Default: False

--guided-decoding-disable-any-whitespace, --no-guided-decoding-disable-any-whitespace

If True, the model will not generate any whitespace during guided decoding. This is only supported for xgrammar and guidance backends.

Default: False

--guided-decoding-disable-additional-properties, --no-guided-decoding-disable-additional-properties

If True, the guidance backend will not use additionalProperties in the JSON schema. This is only supported for the guidance backend and is used to better align its behaviour with outlines and xgrammar.

Default: False

--reasoning-parser

Possible choices: deepseek_r1, granite, hunyuan_a13b, qwen3

Select the reasoning parser depending on the model that you're using. This is used to parse the reasoning content into OpenAI API format.

Default: ``

ParallelConfig

Configuration for the distributed execution.

--distributed-executor-backend

Possible choices: external_launcher, mp, ray, uni, None

Backend to use for distributed model workers, either "ray" or "mp" (multiprocessing). If the product of pipeline_parallel_size and tensor_parallel_size is less than or equal to the number of GPUs available, "mp" will be used to keep processing on a single host. Otherwise, this will default to "ray" if Ray is installed and fail otherwise. Note that tpu only support Ray for distributed inference.

Default: None

--pipeline-parallel-size, -pp

Number of pipeline parallel groups.

Default: 1

--tensor-parallel-size, -tp

Number of tensor parallel groups.

Default: 1

--data-parallel-size, -dp

Number of data parallel groups. MoE layers will be sharded according to the product of the tensor parallel size and data parallel size.

Default: 1

--data-parallel-rank, -dpn

Data parallel rank of this instance. When set, enables external load balancer mode.

Default: None

--data-parallel-size-local, -dpl

Number of data parallel replicas to run on this node.

Default: None

--data-parallel-address, -dpa

Address of data parallel cluster head-node.

Default: None

--data-parallel-rpc-port, -dpp

Port for data parallel RPC communication.

Default: None

--data-parallel-backend, -dpb

Backend for data parallel, either "mp" or "ray".

Default: mp

--enable-expert-parallel, --no-enable-expert-parallel

Use expert parallelism instead of tensor parallelism for MoE layers.

Default: False

--enable-eplb, --no-enable-eplb

Enable expert parallelism load balancing for MoE layers.

Default: False

--num-redundant-experts

Number of redundant experts to use for expert parallelism.

Default: 0

--eplb-window-size

Window size for expert load recording.

Default: 1000

--eplb-step-interval

Interval for rearranging experts in expert parallelism.

Note that if this is greater than the EPLB window size, only the metrics of the last eplb_window_size steps will be used for rearranging experts.

Default: 3000

--eplb-log-balancedness, --no-eplb-log-balancedness

Log the balancedness each step of expert parallelism. This is turned off by default since it will cause communication overhead.

Default: False

--max-parallel-loading-workers

Maximum number of parallel loading workers when loading model sequentially in multiple batches. To avoid RAM OOM when using tensor parallel and large models.

Default: None

--ray-workers-use-nsight, --no-ray-workers-use-nsight

Whether to profile Ray workers with nsight, see https://docs.ray.io/en/latest/ray-observability/user-guides/profiling.html#profiling-nsight-profiler.

Default: False

--disable-custom-all-reduce, --no-disable-custom-all-reduce

Disable the custom all-reduce kernel and fall back to NCCL.

Default: False

--worker-cls

The full name of the worker class to use. If "auto", the worker class will be determined based on the platform.

Default: auto

--worker-extension-cls

The full name of the worker extension class to use. The worker extension class is dynamically inherited by the worker class. This is used to inject new attributes and methods to the worker class for use in collective_rpc calls.

Default: ``

--enable-multimodal-encoder-data-parallel, --no-enable-multimodal-encoder-data-parallel

Use data parallelism instead of tensor parallelism for vision encoder. Only support LLama4 for now

Default: False

CacheConfig

Configuration for the KV cache.

--block-size

Possible choices: 1, 8, 16, 32, 64, 128

Size of a contiguous cache block in number of tokens. This is ignored on neuron devices and set to --max-model-len. On CUDA devices, only block sizes up to 32 are supported. On HPU devices, block size defaults to 128.

This config has no static default. If left unspecified by the user, it will be set in Platform.check_and_update_config() based on the current platform.

Default: None

--gpu-memory-utilization

The fraction of GPU memory to be used for the model executor, which can range from 0 to 1. For example, a value of 0.5 would imply 50%% GPU memory utilization. If unspecified, will use the default value of 0.9. This is a per-instance limit, and only applies to the current vLLM instance. It does not matter if you have another vLLM instance running on the same GPU. For example, if you have two vLLM instances running on the same GPU, you can set the GPU memory utilization to 0.5 for each instance.

Default: 0.9

--swap-space

Size of the CPU swap space per GPU (in GiB).

Default: 4

--kv-cache-dtype

Possible choices: auto, fp8, fp8_e4m3, fp8_e5m2, fp8_inc

Data type for kv cache storage. If "auto", will use model data type. CUDA 11.8+ supports fp8 (=fp8_e4m3) and fp8_e5m2. ROCm (AMD GPU) supports fp8 (=fp8_e4m3). Intel Gaudi (HPU) supports fp8 (using fp8_inc).

Default: auto

--num-gpu-blocks-override

Number of GPU blocks to use. This overrides the profiled num_gpu_blocks if specified. Does nothing if None. Used for testing preemption.

Default: None

--enable-prefix-caching, --no-enable-prefix-caching

Whether to enable prefix caching. Disabled by default for V0. Enabled by default for V1.

Default: None

--prefix-caching-hash-algo

Possible choices: builtin, sha256, sha256_cbor_64bit

Set the hash algorithm for prefix caching:

  • "builtin" is Python's built-in hash.

  • "sha256" is collision resistant but with certain overheads. This option uses Pickle for object serialization before hashing.

  • "sha256_cbor_64bit" provides a reproducible, cross-language compatible hash. It serializes objects using canonical CBOR and hashes them with SHA-256. The resulting hash consists of the lower 64 bits of the SHA-256 digest.

Default: builtin

--cpu-offload-gb

The space in GiB to offload to CPU, per GPU. Default is 0, which means no offloading. Intuitively, this argument can be seen as a virtual way to increase the GPU memory size. For example, if you have one 24 GB GPU and set this to 10, virtually you can think of it as a 34 GB GPU. Then you can load a 13B model with BF16 weight, which requires at least 26GB GPU memory. Note that this requires fast CPU-GPU interconnect, as part of the model is loaded from CPU memory to GPU memory on the fly in each model forward pass.

Default: 0

--calculate-kv-scales, --no-calculate-kv-scales

This enables dynamic calculation of k_scale and v_scale when kv_cache_dtype is fp8. If False, the scales will be loaded from the model checkpoint if available. Otherwise, the scales will default to 1.0.

Default: False

MultiModalConfig

Controls the behavior of multimodal models.

--limit-mm-per-prompt

The maximum number of input items allowed per prompt for each modality. Defaults to 1 (V0) or 999 (V1) for each modality.

For example, to allow up to 16 images and 2 videos per prompt: {"images": 16, "videos": 2}

Should either be a valid JSON string or JSON keys passed individually. For example, the following sets of arguments are equivalent:

  • --json-arg '{"key1": "value1", "key2": {"key3": "value2"}}'

  • --json-arg.key1 value1 --json-arg.key2.key3 value2

Additionally, list elements can be passed individually using +:

  • --json-arg '{"key4": ["value3", "value4", "value5"]}'

  • --json-arg.key4+ value3 --json-arg.key4+='value4,value5'

Default: {}

--media-io-kwargs

Additional args passed to process media inputs, keyed by modalities. For example, to set num_frames for video, set --media-io-kwargs '{"video": {"num_frames": 40} }'

Should either be a valid JSON string or JSON keys passed individually. For example, the following sets of arguments are equivalent:

  • --json-arg '{"key1": "value1", "key2": {"key3": "value2"}}'

  • --json-arg.key1 value1 --json-arg.key2.key3 value2

Additionally, list elements can be passed individually using +:

  • --json-arg '{"key4": ["value3", "value4", "value5"]}'

  • --json-arg.key4+ value3 --json-arg.key4+='value4,value5'

Default: {}

--mm-processor-kwargs

Overrides for the multi-modal processor obtained from transformers.AutoProcessor.from_pretrained.

The available overrides depend on the model that is being run.

For example, for Phi-3-Vision: {"num_crops": 4}.

Should either be a valid JSON string or JSON keys passed individually. For example, the following sets of arguments are equivalent:

  • --json-arg '{"key1": "value1", "key2": {"key3": "value2"}}'

  • --json-arg.key1 value1 --json-arg.key2.key3 value2

Additionally, list elements can be passed individually using +:

  • --json-arg '{"key4": ["value3", "value4", "value5"]}'

  • --json-arg.key4+ value3 --json-arg.key4+='value4,value5'

Default: None

--disable-mm-preprocessor-cache, --no-disable-mm-preprocessor-cache

If True, disable caching of the processed multi-modal inputs.

Default: False

--interleave-mm-strings, --no-interleave-mm-strings

Enable fully interleaved support for multimodal prompts.

Default: False

LoRAConfig

Configuration for LoRA.

--enable-lora, --no-enable-lora

If True, enable handling of LoRA adapters.

Default: None

--enable-lora-bias, --no-enable-lora-bias

Enable bias for LoRA adapters.

Default: False

--max-loras

Max number of LoRAs in a single batch.

Default: 1

--max-lora-rank

Max LoRA rank.

Default: 16

--lora-extra-vocab-size

Maximum size of extra vocabulary that can be present in a LoRA adapter (added to the base model vocabulary).

Default: 256

--lora-dtype

Possible choices: auto, bfloat16, float16

Data type for LoRA. If auto, will default to base model dtype.

Default: auto

--long-lora-scaling-factors

Specify multiple scaling factors (which can be different from base model scaling factor - see eg. Long LoRA) to allow for multiple LoRA adapters trained with those scaling factors to be used at the same time. If not specified, only adapters trained with the base model scaling factor are allowed.

Default: None

--max-cpu-loras

Maximum number of LoRAs to store in CPU memory. Must be >= than max_loras.

Default: None

--fully-sharded-loras, --no-fully-sharded-loras

By default, only half of the LoRA computation is sharded with tensor parallelism. Enabling this will use the fully sharded layers. At high sequence length, max rank or tensor parallel size, this is likely faster.

Default: False

--default-mm-loras

Dictionary mapping specific modalities to LoRA model paths; this field is only applicable to multimodal models and should be leveraged when a model always expects a LoRA to be active when a given modality is present. Note that currently, if a request provides multiple additional modalities, each of which have their own LoRA, we do NOT apply default_mm_loras because we currently only support one lora adapter per prompt. When run in offline mode, the lora IDs for n modalities will be automatically assigned to 1-n with the names of the modalities in alphabetic order.

Should either be a valid JSON string or JSON keys passed individually. For example, the following sets of arguments are equivalent:

  • --json-arg '{"key1": "value1", "key2": {"key3": "value2"}}'

  • --json-arg.key1 value1 --json-arg.key2.key3 value2

Additionally, list elements can be passed individually using +:

  • --json-arg '{"key4": ["value3", "value4", "value5"]}'

  • --json-arg.key4+ value3 --json-arg.key4+='value4,value5'

Default: None

PromptAdapterConfig

Configuration for PromptAdapters.

--enable-prompt-adapter, --no-enable-prompt-adapter

If True, enable handling of PromptAdapters.

Default: None

--max-prompt-adapters

Max number of PromptAdapters in a batch.

Default: 1

--max-prompt-adapter-token

Max number of PromptAdapters tokens.

Default: 0

DeviceConfig

Configuration for the device to use for vLLM execution.

--device

Possible choices: auto, cpu, cuda, hpu, neuron, tpu, xpu, None

Device type for vLLM execution. This parameter is deprecated and will be removed in a future release. It will now be set automatically based on the current platform.

Default: auto

SpeculativeConfig

Configuration for speculative decoding.

--speculative-config

The configurations for speculative decoding. Should be a JSON string.

Default: None

ObservabilityConfig

Configuration for observability - metrics and tracing.

--show-hidden-metrics-for-version

Enable deprecated Prometheus metrics that have been hidden since the specified version. For example, if a previously deprecated metric has been hidden since the v0.7.0 release, you use --show-hidden-metrics-for-version=0.7 as a temporary escape hatch while you migrate to new metrics. The metric is likely to be removed completely in an upcoming release.

Default: None

--otlp-traces-endpoint

Target URL to which OpenTelemetry traces will be sent.

Default: None

--collect-detailed-traces

Possible choices: all, model, worker, None, model,worker, model,all, worker,model, worker,all, all,model, all,worker

It makes sense to set this only if --otlp-traces-endpoint is set. If set, it will collect detailed traces for the specified modules. This involves use of possibly costly and or blocking operations and hence might have a performance impact.

Note that collecting detailed timing information for each request can be expensive.

Default: None

SchedulerConfig

Scheduler configuration.

--max-num-batched-tokens

Maximum number of tokens to be processed in a single iteration.

This config has no static default. If left unspecified by the user, it will be set in EngineArgs.create_engine_config based on the usage context.

Default: None

--max-num-seqs

Maximum number of sequences to be processed in a single iteration.

This config has no static default. If left unspecified by the user, it will be set in EngineArgs.create_engine_config based on the usage context.

Default: None

--max-num-partial-prefills

For chunked prefill, the maximum number of sequences that can be partially prefilled concurrently.

Default: 1

--max-long-partial-prefills

For chunked prefill, the maximum number of prompts longer than long_prefill_token_threshold that will be prefilled concurrently. Setting this less than max_num_partial_prefills will allow shorter prompts to jump the queue in front of longer prompts in some cases, improving latency.

Default: 1

--cuda-graph-sizes

Cuda graph capture sizes 1. if none provided, then default set to [min(max_num_seqs * 2, 512)] 2. if one value is provided, then the capture list would follow the pattern: [1, 2, 4] + [i for i in range(8, cuda_graph_sizes + 1, 8)] 3. more than one value (e.g. 1 2 128) is provided, then the capture list will follow the provided list.

Default: []

--long-prefill-token-threshold

For chunked prefill, a request is considered long if the prompt is longer than this number of tokens.

Default: 0

--num-lookahead-slots

The number of slots to allocate per sequence per step, beyond the known token ids. This is used in speculative decoding to store KV activations of tokens which may or may not be accepted.

NOTE: This will be replaced by speculative config in the future; it is present to enable correctness tests until then.

Default: 0

--scheduler-delay-factor

Apply a delay (of delay factor multiplied by previous prompt latency) before scheduling next prompt.

Default: 0.0

--preemption-mode

Possible choices: recompute, swap, None

Whether to perform preemption by swapping or recomputation. If not specified, we determine the mode as follows: We use recomputation by default since it incurs lower overhead than swapping. However, when the sequence group has multiple sequences (e.g., beam search), recomputation is not currently supported. In such a case, we use swapping instead.

Default: None

--num-scheduler-steps

Maximum number of forward steps per scheduler call.

Default: 1

--multi-step-stream-outputs, --no-multi-step-stream-outputs

If False, then multi-step will stream outputs at the end of all steps

Default: True

--scheduling-policy

Possible choices: fcfs, priority

The scheduling policy to use:

  • "fcfs" means first come first served, i.e. requests are handled in order of arrival.

  • "priority" means requests are handled based on given priority (lower value means earlier handling) and time of arrival deciding any ties).

Default: fcfs

--enable-chunked-prefill, --no-enable-chunked-prefill

If True, prefill requests can be chunked based on the remaining max_num_batched_tokens.

Default: None

--disable-chunked-mm-input, --no-disable-chunked-mm-input

If set to true and chunked prefill is enabled, we do not want to partially schedule a multimodal item. Only used in V1 This ensures that if a request has a mixed prompt (like text tokens TTTT followed by image tokens IIIIIIIIII) where only some image tokens can be scheduled (like TTTTIIIII, leaving IIIII), it will be scheduled as TTTT in one step and IIIIIIIIII in the next.

Default: False

--scheduler-cls

The scheduler class to use. "vllm.core.scheduler.Scheduler" is the default scheduler. Can be a class directly or the path to a class of form "mod.custom_class".

Default: vllm.core.scheduler.Scheduler

--disable-hybrid-kv-cache-manager, --no-disable-hybrid-kv-cache-manager

If set to True, KV cache manager will allocate the same size of KV cache for all attention layers even if there are multiple type of attention layers like full attention and sliding window attention.

Default: False

--async-scheduling, --no-async-scheduling

EXPERIMENTAL: If set to True, perform async scheduling. This may help reduce the CPU overheads, leading to better latency and throughput. However, async scheduling is currently not supported with some features such as structured outputs, speculative decoding, and pipeline parallelism.

Default: False

VllmConfig

Dataclass which contains all vllm-related configuration. This simplifies passing around the distinct configurations in the codebase.

--kv-transfer-config

The configurations for distributed KV cache transfer.

Should either be a valid JSON string or JSON keys passed individually. For example, the following sets of arguments are equivalent:

  • --json-arg '{"key1": "value1", "key2": {"key3": "value2"}}'

  • --json-arg.key1 value1 --json-arg.key2.key3 value2

Additionally, list elements can be passed individually using +:

  • --json-arg '{"key4": ["value3", "value4", "value5"]}'

  • --json-arg.key4+ value3 --json-arg.key4+='value4,value5'

Default: None

--kv-events-config

The configurations for event publishing.

Should either be a valid JSON string or JSON keys passed individually. For example, the following sets of arguments are equivalent:

  • --json-arg '{"key1": "value1", "key2": {"key3": "value2"}}'

  • --json-arg.key1 value1 --json-arg.key2.key3 value2

Additionally, list elements can be passed individually using +:

  • --json-arg '{"key4": ["value3", "value4", "value5"]}'

  • --json-arg.key4+ value3 --json-arg.key4+='value4,value5'

Default: None

--compilation-config, -O

torch.compile and cudagraph capture configuration for the model.

As a shorthand, -O<n> can be used to directly specify the compilation level n: -O3 is equivalent to -O.level=3 (same as -O='{"level":3}'). Currently, -O and -O= are supported as well but this will likely be removed in favor of clearer -O syntax in the future.

NOTE: level 0 is the default level without any optimization. level 1 and 2 are for internal testing only. level 3 is the recommended level for production, also default in V1.

You can specify the full compilation config like so: {"level": 3, "cudagraph_capture_sizes": [1, 2, 4, 8]}

Should either be a valid JSON string or JSON keys passed individually. For example, the following sets of arguments are equivalent:

  • --json-arg '{"key1": "value1", "key2": {"key3": "value2"}}'

  • --json-arg.key1 value1 --json-arg.key2.key3 value2

Additionally, list elements can be passed individually using +:

  • --json-arg '{"key4": ["value3", "value4", "value5"]}'

  • --json-arg.key4+ value3 --json-arg.key4+='value4,value5'

Default: {"level":0,"debug_dump_path":"","cache_dir":"","backend":"","custom_ops":[],"splitting_ops":[],"use_inductor":true,"compile_sizes":null,"inductor_compile_config":{"enable_auto_functionalized_v2":false},"inductor_passes":{},"use_cudagraph":true,"cudagraph_num_of_warmups":0,"cudagraph_capture_sizes":null,"cudagraph_copy_inputs":false,"full_cuda_graph":false,"max_capture_size":null,"local_cache_dir":null}

--additional-config

Additional config for specified platform. Different platforms may support different configs. Make sure the configs are valid for the platform you are using. Contents must be hashable.

Default: {}

AsyncEngineArgs

--disable-log-requests

Disable logging requests.

Default: False