Server Arguments

This page provides a list of server arguments used in the command line to configure the behavior and performance of the language model server during deployment. These arguments enable users to customize key aspects of the server, including model selection, parallelism policies, memory management, and optimization techniques. You can find all arguments by python3 -m sglang.launch_server --help

Common launch commands

• To use a configuration file, create a YAML file with your server arguments and specify it with --config. CLI arguments will override config file values.

  # Create config.yaml
  cat > config.yaml << EOF
  model-path: meta-llama/Meta-Llama-3-8B-Instruct
  host: 0.0.0.0
  port: 30000
  tensor-parallel-size: 2
  enable-metrics: true
  log-requests: true
  EOF
  
  # Launch server with config file
  python -m sglang.launch_server --config config.yaml
  

• To enable multi-GPU tensor parallelism, add --tp 2. If it reports the error “peer access is not supported between these two devices”, add --enable-p2p-check to the server launch command.

  python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --tp 2
  

• To enable multi-GPU data parallelism, add --dp 2. Data parallelism is better for throughput if there is enough memory. It can also be used together with tensor parallelism. The following command uses 4 GPUs in total. We recommend SGLang Model Gateway (former Router) for data parallelism.

  python -m sglang_router.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --dp 2 --tp 2
  

• If you see out-of-memory errors during serving, try to reduce the memory usage of the KV cache pool by setting a smaller value of --mem-fraction-static. The default value is 0.9.

  python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --mem-fraction-static 0.7
  

• See hyperparameter tuning on tuning hyperparameters for better performance.

• For docker and Kubernetes runs, you need to set up shared memory which is used for communication between processes. See --shm-size for docker and /dev/shm size update for Kubernetes manifests.

• If you see out-of-memory errors during prefill for long prompts, try to set a smaller chunked prefill size.

  python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct --chunked-prefill-size 4096
  

• To enable fp8 weight quantization, add --quantization fp8 on a fp16 checkpoint or directly load a fp8 checkpoint without specifying any arguments.

• To enable fp8 kv cache quantization, add --kv-cache-dtype fp8_e4m3 or --kv-cache-dtype fp8_e5m2.

• To enable deterministic inference and batch invariant operations, add --enable-deterministic-inference. More details can be found in deterministic inference document.

• If the model does not have a chat template in the Hugging Face tokenizer, you can specify a custom chat template. If the tokenizer has multiple named templates (e.g., ‘default’, ‘tool_use’), you can select one using --hf-chat-template-name tool_use.

• To run tensor parallelism on multiple nodes, add --nnodes 2. If you have two nodes with two GPUs on each node and want to run TP=4, let sgl-dev-0 be the hostname of the first node and 50000 be an available port, you can use the following commands. If you meet deadlock, please try to add --disable-cuda-graph

• (Note: This feature is out of maintenance and might cause error) To enable torch.compile acceleration, add --enable-torch-compile. It accelerates small models on small batch sizes. By default, the cache path is located at /tmp/torchinductor_root, you can customize it using environment variable TORCHINDUCTOR_CACHE_DIR. For more details, please refer to PyTorch official documentation and Enabling cache for torch.compile.

  # Node 0
  python -m sglang.launch_server \
    --model-path meta-llama/Meta-Llama-3-8B-Instruct \
    --tp 4 \
    --dist-init-addr sgl-dev-0:50000 \
    --nnodes 2 \
    --node-rank 0
  
  # Node 1
  python -m sglang.launch_server \
    --model-path meta-llama/Meta-Llama-3-8B-Instruct \
    --tp 4 \
    --dist-init-addr sgl-dev-0:50000 \
    --nnodes 2 \
    --node-rank 1
  

Please consult the documentation below and server_args.py to learn more about the arguments you may provide when launching a server.

Model and tokenizer

Argument	Description	Defaults	Options
--model-path --model	The path of the model weights. This can be a local folder or a Hugging Face repo ID.	None	Type: str
--tokenizer-path	The path of the tokenizer.	None	Type: str
--tokenizer-mode	Tokenizer mode. ‘auto’ will use the fast tokenizer if available, and ‘slow’ will always use the slow tokenizer.	auto	auto, slow
--tokenizer-worker-num	The worker num of the tokenizer manager.	1	Type: int
--skip-tokenizer-init	If set, skip init tokenizer and pass input_ids in generate request.	False	bool flag (set to enable)
--load-format	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.”gguf” will load the weights in the gguf format. “bitsandbytes” will load the weights using bitsandbytes quantization.”layered” loads weights layer by layer so that one can quantize a layer before loading another to make the peak memory envelope smaller. “flash_rl” will load the weights in flash_rl format. “fastsafetensors” and “private” are also supported.	auto	auto, pt, safetensors, npcache, dummy, sharded_state, gguf, bitsandbytes, layered, flash_rl, remote, remote_instance, fastsafetensors, private
--model-loader-extra-config	Extra config for model loader. This will be passed to the model loader corresponding to the chosen load_format.	{}	Type: str
--trust-remote-code	Whether or not to allow for custom models defined on the Hub in their own modeling files.	False	bool flag (set to enable)
--context-length	The model’s maximum context length. Defaults to None (will use the value from the model’s config.json instead).	None	Type: int
--is-embedding	Whether to use a CausalLM as an embedding model.	False	bool flag (set to enable)
--enable-multimodal	Enable the multimodal functionality for the served model. If the model being served is not multimodal, nothing will happen	None	bool flag (set to enable)
--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.	None	Type: str
--model-impl	Which implementation of the model to use. * “auto” will try to use the SGLang implementation if it exists and fall back to the Transformers implementation if no SGLang implementation is available. * “sglang” will use the SGLang model implementation. * “transformers” will use the Transformers model implementation.	auto	Type: str

HTTP server

Argument	Description	Defaults	Options
--host	The host of the HTTP server.	127.0.0.1	Type: str
--port	The port of the HTTP server.	30000	Type: int
--fastapi-root-path	App is behind a path based routing proxy.	""	Type: str
--grpc-mode	If set, use gRPC server instead of HTTP server.	False	bool flag (set to enable)
--skip-server-warmup	If set, skip warmup.	False	bool flag (set to enable)
--warmups	Specify custom warmup functions (csv) to run before server starts eg. –warmups=warmup_name1,warmup_name2 will run the functions warmup_name1 and warmup_name2 specified in warmup.py before the server starts listening for requests	None	Type: str
--nccl-port	The port for NCCL distributed environment setup. Defaults to a random port.	None	Type: int
--checkpoint-engine-wait-weights-before-ready	If set, the server will wait for initial weights to be loaded via checkpoint-engine or other update methods before serving inference requests.	False	bool flag (set to enable)

Quantization and data type

Argument	Description	Defaults	Options
--dtype	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.	auto	auto, half, float16, bfloat16, float, float32
--quantization	The quantization method.	None	awq, fp8, gptq, marlin, gptq_marlin, awq_marlin, bitsandbytes, gguf, modelopt, modelopt_fp8, modelopt_fp4, petit_nvfp4, w8a8_int8, w8a8_fp8, moe_wna16, qoq, w4afp8, mxfp4, mxfp8, auto-round, compressed-tensors, modelslim, quark_int4fp8_moe
--quantization-param-path	Path to the JSON file containing the KV cache scaling factors. This should generally be supplied, when KV cache dtype is FP8. Otherwise, KV cache scaling factors default to 1.0, which may cause accuracy issues.	None	Type: Optional[str]
--kv-cache-dtype	Data type for kv cache storage. “auto” will use model data type. “bf16” or “bfloat16” for BF16 KV cache. “fp8_e5m2” and “fp8_e4m3” are supported for CUDA 11.8+. “fp4_e2m1” (only mxfp4) is supported for CUDA 12.8+ and PyTorch 2.8.0+	auto	auto, fp8_e5m2, fp8_e4m3, bf16, bfloat16, fp4_e2m1
--enable-fp32-lm-head	If set, the LM head outputs (logits) are in FP32.	False	bool flag (set to enable)
--modelopt-quant	The ModelOpt quantization configuration. Supported values: ‘fp8’, ‘int4_awq’, ‘w4a8_awq’, ‘nvfp4’, ‘nvfp4_awq’. This requires the NVIDIA Model Optimizer library to be installed: pip install nvidia-modelopt	None	Type: str
--modelopt-checkpoint-restore-path	Path to restore a previously saved ModelOpt quantized checkpoint. If provided, the quantization process will be skipped and the model will be loaded from this checkpoint.	None	Type: str
--modelopt-checkpoint-save-path	Path to save the ModelOpt quantized checkpoint after quantization. This allows reusing the quantized model in future runs.	None	Type: str
--modelopt-export-path	Path to export the quantized model in HuggingFace format after ModelOpt quantization. The exported model can then be used directly with SGLang for inference. If not provided, the model will not be exported.	None	Type: str
--quantize-and-serve	Quantize the model with ModelOpt and immediately serve it without exporting. This is useful for development and prototyping. For production, it’s recommended to use separate quantization and deployment steps.	False	bool flag (set to enable)
--rl-quant-profile	Path to the FlashRL quantization profile. Required when using –load-format flash_rl.	None	Type: str

Memory and scheduling

Argument	Description	Defaults	Options
--mem-fraction-static	The fraction of the memory used for static allocation (model weights and KV cache memory pool). Use a smaller value if you see out-of-memory errors.	None	Type: float
--max-running-requests	The maximum number of running requests.	None	Type: int
--max-queued-requests	The maximum number of queued requests. This option is ignored when using disaggregation-mode.	None	Type: int
--max-total-tokens	The maximum number of tokens in the memory pool. If not specified, it will be automatically calculated based on the memory usage fraction. This option is typically used for development and debugging purposes.	None	Type: int
--chunked-prefill-size	The maximum number of tokens in a chunk for the chunked prefill. Setting this to -1 means disabling chunked prefill.	None	Type: int
--prefill-max-requests	The maximum number of requests in a prefill batch. If not specified, there is no limit.	None	Type: int
--enable-dynamic-chunking	Enable dynamic chunk size adjustment for pipeline parallelism. When enabled, chunk sizes are dynamically calculated based on fitted function to maintain consistent execution time across chunks.	False	bool flag (set to enable)
--max-prefill-tokens	The maximum number of tokens in a prefill batch. The real bound will be the maximum of this value and the model’s maximum context length.	16384	Type: int
--schedule-policy	The scheduling policy of the requests.	fcfs	lpm, random, fcfs, dfs-weight, lof, priority, routing-key
--enable-priority-scheduling	Enable priority scheduling. Requests with higher priority integer values will be scheduled first by default.	False	bool flag (set to enable)
--abort-on-priority-when-disabled	If set, abort requests that specify a priority when priority scheduling is disabled.	False	bool flag (set to enable)
--schedule-low-priority-values-first	If specified with –enable-priority-scheduling, the scheduler will schedule requests with lower priority integer values first.	False	bool flag (set to enable)
--priority-scheduling-preemption-threshold	Minimum difference in priorities for an incoming request to have to preempt running request(s).	10	Type: int
--schedule-conservativeness	How conservative the schedule policy is. A larger value means more conservative scheduling. Use a larger value if you see requests being retracted frequently.	1.0	Type: float
--page-size	The number of tokens in a page.	1	Type: int
--swa-full-tokens-ratio	The ratio of SWA layer KV tokens / full layer KV tokens, regardless of the number of swa:full layers. It should be between 0 and 1. E.g. 0.5 means if each swa layer has 50 tokens, then each full layer has 100 tokens.	0.8	Type: float
--disable-hybrid-swa-memory	Disable the hybrid SWA memory.	False	bool flag (set to enable)
--radix-eviction-policy	The eviction policy of radix trees. ‘lru’ stands for Least Recently Used, ‘lfu’ stands for Least Frequently Used.	lru	lru, lfu
--enable-prefill-delayer	Enable prefill delayer for DP attention to reduce idle time.	False	bool flag (set to enable)
--prefill-delayer-max-delay-passes	Maximum forward passes to delay prefill.	30	Type: int
--prefill-delayer-token-usage-low-watermark	Token usage low watermark for prefill delayer.	None	Type: float
--prefill-delayer-forward-passes-buckets	Custom buckets for prefill delayer forward passes histogram. 0 and max_delay_passes-1 will be auto-added.	None	List[float]
--prefill-delayer-wait-seconds-buckets	Custom buckets for prefill delayer wait seconds histogram. 0 will be auto-added.	None	List[float]

Runtime options

Argument	Description	Defaults	Options
--device	The device to use (‘cuda’, ‘xpu’, ‘hpu’, ‘npu’, ‘cpu’). Defaults to auto-detection if not specified.	None	Type: str
--tensor-parallel-size --tp-size	The tensor parallelism size.	1	Type: int
--pipeline-parallel-size --pp-size	The pipeline parallelism size.	1	Type: int
--attention-context-parallel-size --attn-cp-size	The attention context parallelism size.	1	Type: int
--moe-data-parallel-size --moe-dp-size	The moe data parallelism size.	1	Type: int
--pp-max-micro-batch-size	The maximum micro batch size in pipeline parallelism.	None	Type: int
--pp-async-batch-depth	The async batch depth of pipeline parallelism.	0	Type: int
--stream-interval	The interval (or buffer size) for streaming in terms of the token length. A smaller value makes streaming smoother, while a larger value makes the throughput higher	1	Type: int
--stream-output	Whether to output as a sequence of disjoint segments.	False	bool flag (set to enable)
--random-seed	The random seed.	None	Type: int
--constrained-json-whitespace-pattern	(outlines and llguidance backends only) Regex pattern for syntactic whitespaces allowed in JSON constrained output. For example, to allow the model to generate consecutive whitespaces, set the pattern to [\n\t ]*	None	Type: str
--constrained-json-disable-any-whitespace	(xgrammar and llguidance backends only) Enforce compact representation in JSON constrained output.	False	bool flag (set to enable)
--watchdog-timeout	Set watchdog timeout in seconds. If a forward batch takes longer than this, the server will crash to prevent hanging.	300	Type: float
--soft-watchdog-timeout	Set soft watchdog timeout in seconds. If a forward batch takes longer than this, the server will dump information for debugging.	None	Type: float
--dist-timeout	Set timeout for torch.distributed initialization.	None	Type: int
--download-dir	Model download directory for huggingface.	None	Type: str
--model-checksum	Model file integrity verification. If provided without value, uses model-path as HF repo ID. Otherwise, provide checksums JSON file path or HuggingFace repo ID.	None	Type: str
--base-gpu-id	The base GPU ID to start allocating GPUs from. Useful when running multiple instances on the same machine.	0	Type: int
--gpu-id-step	The delta between consecutive GPU IDs that are used. For example, setting it to 2 will use GPU 0,2,4,…	1	Type: int
--sleep-on-idle	Reduce CPU usage when sglang is idle.	False	bool flag (set to enable)
--custom-sigquit-handler	Register a custom sigquit handler so you can do additional cleanup after the server is shutdown. This is only available for Engine, not for CLI.	None	Type: str

Logging

Argument	Description	Defaults	Options
--log-level	The logging level of all loggers.	info	Type: str
--log-level-http	The logging level of HTTP server. If not set, reuse –log-level by default.	None	Type: str
--log-requests	Log metadata, inputs, outputs of all requests. The verbosity is decided by –log-requests-level	False	bool flag (set to enable)
--log-requests-level	0: Log metadata (no sampling parameters). 1: Log metadata and sampling parameters. 2: Log metadata, sampling parameters and partial input/output. 3: Log every input/output.	2	0, 1, 2, 3
--log-requests-format	Format for request logging: ‘text’ (human-readable) or ‘json’ (structured)	text	text, json
--log-requests-target	Target(s) for request logging: ‘stdout’ and/or directory path(s) for file output. Can specify multiple targets, e.g., ‘–log-requests-target stdout /my/path’.	None	List[str]
--uvicorn-access-log-exclude-prefixes	Exclude uvicorn access logs whose request path starts with any of these prefixes. Defaults to empty (disabled).	[]	List[str]
--crash-dump-folder	Folder path to dump requests from the last 5 min before a crash (if any). If not specified, crash dumping is disabled.	None	Type: str
--show-time-cost	Show time cost of custom marks.	False	bool flag (set to enable)
--enable-metrics	Enable log prometheus metrics.	False	bool flag (set to enable)
--enable-metrics-for-all-schedulers	Enable –enable-metrics-for-all-schedulers when you want schedulers on all TP ranks (not just TP 0) to record request metrics separately. This is especially useful when dp_attention is enabled, as otherwise all metrics appear to come from TP 0.	False	bool flag (set to enable)
--tokenizer-metrics-custom-labels-header	Specify the HTTP header for passing custom labels for tokenizer metrics.	x-custom-labels	Type: str
--tokenizer-metrics-allowed-custom-labels	The custom labels allowed for tokenizer metrics. The labels are specified via a dict in ‘–tokenizer-metrics-custom-labels-header’ field in HTTP requests, e.g., {‘label1’: ‘value1’, ‘label2’: ‘value2’} is allowed if ‘–tokenizer-metrics-allowed-custom-labels label1 label2’ is set.	None	List[str]
--bucket-time-to-first-token	The buckets of time to first token, specified as a list of floats.	None	List[float]
--bucket-inter-token-latency	The buckets of inter-token latency, specified as a list of floats.	None	List[float]
--bucket-e2e-request-latency	The buckets of end-to-end request latency, specified as a list of floats.	None	List[float]
--collect-tokens-histogram	Collect prompt/generation tokens histogram.	False	bool flag (set to enable)
--prompt-tokens-buckets	The buckets rule of prompt tokens. Supports 3 rule types: ‘default’ uses predefined buckets; ‘tse ’ generates two sides exponential distributed buckets (e.g., ‘tse 1000 2 8’ generates buckets [984.0, 992.0, 996.0, 998.0, 1000.0, 1002.0, 1004.0, 1008.0, 1016.0]).); ‘custom …’ uses custom bucket values (e.g., ‘custom 10 50 100 500’).	None	List[str]
--generation-tokens-buckets	The buckets rule for generation tokens histogram. Supports 3 rule types: ‘default’ uses predefined buckets; ‘tse ’ generates two sides exponential distributed buckets (e.g., ‘tse 1000 2 8’ generates buckets [984.0, 992.0, 996.0, 998.0, 1000.0, 1002.0, 1004.0, 1008.0, 1016.0]).); ‘custom …’ uses custom bucket values (e.g., ‘custom 10 50 100 500’).	None	List[str]
--gc-warning-threshold-secs	The threshold for long GC warning. If a GC takes longer than this, a warning will be logged. Set to 0 to disable.	0.0	Type: float
--decode-log-interval	The log interval of decode batch.	40	Type: int
--enable-request-time-stats-logging	Enable per request time stats logging	False	bool flag (set to enable)
--kv-events-config	Config in json format for NVIDIA dynamo KV event publishing. Publishing will be enabled if this flag is used.	None	Type: str
--enable-trace	Enable opentelemetry trace	False	bool flag (set to enable)
--otlp-traces-endpoint	Config opentelemetry collector endpoint if –enable-trace is set. format: :	localhost:4317	Type: str

RequestMetricsExporter configuration

Argument	Description	Defaults	Options
--export-metrics-to-file	Export performance metrics for each request to local file (e.g. for forwarding to external systems).	False	bool flag (set to enable)
--export-metrics-to-file-dir	Directory path for writing performance metrics files (required when –export-metrics-to-file is enabled).	None	Type: str

API related

Argument	Description	Defaults	Options
--api-key	Set API key of the server. It is also used in the OpenAI API compatible server.	None	Type: str
--admin-api-key	Set admin API key for administrative/control endpoints (e.g., weights update, cache flush, /get_server_info). Endpoints marked as admin-only require Authorization: Bearer <admin_api_key> when this is set.	None	Type: str
--served-model-name	Override the model name returned by the v1/models endpoint in OpenAI API server.	None	Type: str
--weight-version	Version identifier for the model weights. Defaults to ‘default’ if not specified.	default	Type: str
--chat-template	The builtin chat template name or the path of the chat template file. This is only used for OpenAI-compatible API server.	None	Type: str
--hf-chat-template-name	When the HuggingFace tokenizer has multiple chat templates (e.g., ‘default’, ‘tool_use’, ‘rag’), specify which named template to use. If not set, the first available template is used.	None	Type: str
--completion-template	The builtin completion template name or the path of the completion template file. This is only used for OpenAI-compatible API server. only for code completion currently.	None	Type: str
--file-storage-path	The path of the file storage in backend.	sglang_storage	Type: str
--enable-cache-report	Return number of cached tokens in usage.prompt_tokens_details for each openai request.	False	bool flag (set to enable)
--reasoning-parser	Specify the parser for reasoning models. Supported parsers: [deepseek-r1, deepseek-v3, glm45, gpt-oss, kimi, qwen3, qwen3-thinking, step3].	None	deepseek-r1, deepseek-v3, glm45, gpt-oss, kimi, qwen3, qwen3-thinking, step3
--tool-call-parser	Specify the parser for handling tool-call interactions. Supported parsers: [deepseekv3, deepseekv31, glm, glm45, glm47, gpt-oss, kimi_k2, llama3, mistral, pythonic, qwen, qwen25, qwen3_coder, step3].	None	deepseekv3, deepseekv31, glm, glm45, glm47, gpt-oss, kimi_k2, llama3, mistral, pythonic, qwen, qwen25, qwen3_coder, step3, gigachat3
--tool-server	Either ‘demo’ or a comma-separated list of tool server urls to use for the model. If not specified, no tool server will be used.	None	Type: str
--sampling-defaults	Where to get default sampling parameters. ‘openai’ uses SGLang/OpenAI defaults (temperature=1.0, top_p=1.0, etc.). ‘model’ uses the model’s generation_config.json to get the recommended sampling parameters if available. Default is ‘model’.	model	openai, model

Data parallelism

Argument	Description	Defaults	Options
--data-parallel-size --dp-size	The data parallelism size.	1	Type: int
--load-balance-method	The load balancing strategy for data parallelism. The total_tokens algorithm can only be used when DP attention is applied. This algorithm performs load balancing based on the real-time token load of the DP workers.	auto	auto, round_robin, follow_bootstrap_room, total_requests, total_tokens

Multi-node distributed serving

Argument	Description	Defaults	Options
--dist-init-addr --nccl-init-addr	The host address for initializing distributed backend (e.g., 192.168.0.2:25000).	None	Type: str
--nnodes	The number of nodes.	1	Type: int
--node-rank	The node rank.	0	Type: int

Model override args

Argument	Description	Defaults	Options
--json-model-override-args	A dictionary in JSON string format used to override default model configurations.	{}	Type: str
--preferred-sampling-params	json-formatted sampling settings that will be returned in /get_model_info	None	Type: str

LoRA

Argument	Description	Defaults	Options
--enable-lora	Enable LoRA support for the model. This argument is automatically set to True if --lora-paths is provided for backward compatibility.	False	Bool flag (set to enable)
--enable-lora-overlap-loading	Enable asynchronous LoRA weight loading in order to overlap H2D transfers with GPU compute. This should be enabled if you find that your LoRA workloads are bottlenecked by adapter weight loading, for example when frequently loading large LoRA adapters.	False	Bool flag (set to enable)
--max-lora-rank	The maximum LoRA rank that should be supported. If not specified, it will be automatically inferred from the adapters provided in --lora-paths. This argument is needed when you expect to dynamically load adapters of larger LoRA rank after server startup.	None	Type: int
--lora-target-modules	The union set of all target modules where LoRA should be applied (e.g., q_proj, k_proj, gate_proj). If not specified, it will be automatically inferred from the adapters provided in --lora-paths. You can also set it to all to enable LoRA for all supported modules; note this may introduce minor performance overhead.	None	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj, qkv_proj, gate_up_proj, all
--lora-paths	The list of LoRA adapters to load. Each adapter must be specified in one of the following formats: <PATH> | <NAME>=<PATH> | JSON with schema {"lora_name": str, "lora_path": str, "pinned": bool}.	None	Type: List[str] / JSON objects
--max-loras-per-batch	Maximum number of adapters for a running batch, including base-only requests.	8	Type: int
--max-loaded-loras	If specified, limits the maximum number of LoRA adapters loaded in CPU memory at a time. Must be ≥ --max-loras-per-batch.	None	Type: int
--lora-eviction-policy	LoRA adapter eviction policy when the GPU memory pool is full.	lru	lru, fifo
--lora-backend	Choose the kernel backend for multi-LoRA serving.	csgmv	triton, csgmv, ascend, torch_native
--max-lora-chunk-size	Maximum chunk size for the ChunkedSGMV LoRA backend. Only used when --lora-backend is csgmv. Larger values may improve performance.	16	16, 32, 64, 128

Kernel Backends (Attention, Sampling, Grammar, GEMM)

Argument	Description	Defaults	Options
--attention-backend	Choose the kernels for attention layers.	None	triton, torch_native, flex_attention, nsa, cutlass_mla, fa3, fa4, flashinfer, flashmla, trtllm_mla, trtllm_mha, dual_chunk_flash_attn, aiter, wave, intel_amx, ascend
--prefill-attention-backend	Choose the kernels for prefill attention layers (have priority over –attention-backend).	None	triton, torch_native, flex_attention, nsa, cutlass_mla, fa3, fa4, flashinfer, flashmla, trtllm_mla, trtllm_mha, dual_chunk_flash_attn, aiter, wave, intel_amx, ascend
--decode-attention-backend	Choose the kernels for decode attention layers (have priority over –attention-backend).	None	triton, torch_native, flex_attention, nsa, cutlass_mla, fa3, fa4, flashinfer, flashmla, trtllm_mla, trtllm_mha, dual_chunk_flash_attn, aiter, wave, intel_amx, ascend
--sampling-backend	Choose the kernels for sampling layers.	None	flashinfer, pytorch, ascend
--grammar-backend	Choose the backend for grammar-guided decoding.	None	xgrammar, outlines, llguidance, none
--mm-attention-backend	Set multimodal attention backend.	None	sdpa, fa3, fa4, triton_attn, ascend_attn, aiter_attn
--nsa-prefill-backend	Choose the NSA backend for the prefill stage (overrides --attention-backend when running DeepSeek NSA-style attention).	flashmla_sparse	flashmla_sparse, flashmla_kv, flashmla_auto, fa3, tilelang, aiter, trtllm
--nsa-decode-backend	Choose the NSA backend for the decode stage when running DeepSeek NSA-style attention. Overrides --attention-backend for decoding.	fa3	flashmla_sparse, flashmla_kv, fa3, tilelang, aiter, trtllm
--fp8-gemm-backend	Choose the runner backend for Blockwise FP8 GEMM operations. Options: ‘auto’ (default, auto-selects based on hardware), ‘deep_gemm’ (JIT-compiled; enabled by default on NVIDIA Hopper (SM90) and Blackwell (SM100) when DeepGEMM is installed), ‘flashinfer_trtllm’ (optimal for Blackwell and low-latency), ‘flashinfer_deepgemm’ (Hopper SM90 only; uses swapAB optimization for small M dimensions in decoding), ‘cutlass’ (optimal for Hopper/Blackwell GPUs and high-throughput), ‘triton’ (fallback, widely compatible), ‘aiter’ (ROCm only). NOTE: This replaces the deprecated environment variables SGLANG_ENABLE_FLASHINFER_FP8_GEMM and SGLANG_SUPPORT_CUTLASS_BLOCK_FP8.	auto	auto, deep_gemm, flashinfer_trtllm, flashinfer_deepgemm, cutlass, triton, aiter
--fp4-gemm-backend	Choose the runner backend for NVFP4 GEMM operations. Options: ‘flashinfer_cutlass’ (default), ‘auto’ (auto-selects between flashinfer_cudnn/flashinfer_cutlass based on CUDA/cuDNN version), ‘flashinfer_cudnn’ (FlashInfer cuDNN backend, optimal on CUDA 13+ with cuDNN 9.15+), ‘flashinfer_trtllm’ (FlashInfer TensorRT-LLM backend, requires different weight preparation with shuffling). All backends are from FlashInfer; when FlashInfer is unavailable, sgl-kernel CUTLASS is used as an automatic fallback. NOTE: This replaces the deprecated environment variable SGLANG_FLASHINFER_FP4_GEMM_BACKEND.	flashinfer_cutlass	auto, flashinfer_cudnn, flashinfer_cutlass, flashinfer_trtllm
--disable-flashinfer-autotune	Flashinfer autotune is enabled by default. Set this flag to disable the autotune.	False	bool flag (set to enable)

Speculative decoding

Argument	Description	Defaults	Options
--speculative-algorithm	Speculative algorithm.	None	EAGLE, EAGLE3, NEXTN, STANDALONE, NGRAM
--speculative-draft-model-path --speculative-draft-model	The path of the draft model weights. This can be a local folder or a Hugging Face repo ID.	None	Type: str
--speculative-draft-model-revision	The specific draft model version to use. It can be a branch name, a tag name, or a commit id. If unspecified, will use the default version.	None	Type: str
--speculative-draft-load-format	The format of the draft model weights to load. If not specified, will use the same format as –load-format. Use ‘dummy’ to initialize draft model weights with random values for profiling.	None	Same as –load-format options
--speculative-num-steps	The number of steps sampled from draft model in Speculative Decoding.	None	Type: int
--speculative-eagle-topk	The number of tokens sampled from the draft model in eagle2 each step.	None	Type: int
--speculative-num-draft-tokens	The number of tokens sampled from the draft model in Speculative Decoding.	None	Type: int
--speculative-accept-threshold-single	Accept a draft token if its probability in the target model is greater than this threshold.	1.0	Type: float
--speculative-accept-threshold-acc	The accept probability of a draft token is raised from its target probability p to min(1, p / threshold_acc).	1.0	Type: float
--speculative-token-map	The path of the draft model’s small vocab table.	None	Type: str
--speculative-attention-mode	Attention backend for speculative decoding operations (both target verify and draft extend). Can be one of ‘prefill’ (default) or ‘decode’.	prefill	prefill, decode
--speculative-draft-attention-backend	Attention backend for speculative decoding drafting.	None	Same as attention backend options
--speculative-moe-runner-backend	MOE backend for EAGLE speculative decoding, see –moe-runner-backend for options. Same as moe runner backend if unset.	None	Same as –moe-runner-backend options
--speculative-moe-a2a-backend	MOE A2A backend for EAGLE speculative decoding, see –moe-a2a-backend for options. Same as moe a2a backend if unset.	None	Same as –moe-a2a-backend options
--speculative-draft-model-quantization	The quantization method for speculative model.	None	Same as –quantization options

Ngram speculative decoding

Argument	Description	Defaults	Options
--speculative-ngram-min-match-window-size	The minimum window size for pattern matching in ngram speculative decoding.	1	Type: int
--speculative-ngram-max-match-window-size	The maximum window size for pattern matching in ngram speculative decoding.	12	Type: int
--speculative-ngram-min-bfs-breadth	The minimum breadth for BFS (Breadth-First Search) in ngram speculative decoding.	1	Type: int
--speculative-ngram-max-bfs-breadth	The maximum breadth for BFS (Breadth-First Search) in ngram speculative decoding.	10	Type: int
--speculative-ngram-match-type	The match type for cache tree.	BFS	BFS, PROB
--speculative-ngram-branch-length	The branch length for ngram speculative decoding.	18	Type: int
--speculative-ngram-capacity	The cache capacity for ngram speculative decoding.	10000000	Type: int

Multi-layer Eagle speculative decoding

Argument	Description	Defaults	Options
--enable-multi-layer-eagle	Enable multi-layer Eagle speculative decoding.	False	bool flag (set to enable)

MoE

Argument	Description	Defaults	Options
--expert-parallel-size --ep-size --ep	The expert parallelism size.	1	Type: int
--moe-a2a-backend	Select the backend for all-to-all communication for expert parallelism.	none	none, deepep, mooncake, mori, ascend_fuseep
--moe-runner-backend	Choose the runner backend for MoE.	auto	auto, deep_gemm, triton, triton_kernel, flashinfer_trtllm, flashinfer_cutlass, flashinfer_mxfp4, flashinfer_cutedsl, cutlass
--flashinfer-mxfp4-moe-precision	Choose the computation precision of flashinfer mxfp4 moe	default	default, bf16
--enable-flashinfer-allreduce-fusion	Enable FlashInfer allreduce fusion with Residual RMSNorm.	False	bool flag (set to enable)
--deepep-mode	Select the mode when enable DeepEP MoE, could be normal, low_latency or auto. Default is auto, which means low_latency for decode batch and normal for prefill batch.	auto	normal, low_latency, auto
--ep-num-redundant-experts	Allocate this number of redundant experts in expert parallel.	0	Type: int
--ep-dispatch-algorithm	The algorithm to choose ranks for redundant experts in expert parallel.	None	Type: str
--init-expert-location	Initial location of EP experts.	trivial	Type: str
--enable-eplb	Enable EPLB algorithm	False	bool flag (set to enable)
--eplb-algorithm	Chosen EPLB algorithm	auto	Type: str
--eplb-rebalance-num-iterations	Number of iterations to automatically trigger a EPLB re-balance.	1000	Type: int
--eplb-rebalance-layers-per-chunk	Number of layers to rebalance per forward pass.	None	Type: int
--eplb-min-rebalancing-utilization-threshold	Minimum threshold for GPU average utilization to trigger EPLB rebalancing. Must be in the range [0.0, 1.0].	1.0	Type: float
--expert-distribution-recorder-mode	Mode of expert distribution recorder.	None	Type: str
--expert-distribution-recorder-buffer-size	Circular buffer size of expert distribution recorder. Set to -1 to denote infinite buffer.	None	Type: int
--enable-expert-distribution-metrics	Enable logging metrics for expert balancedness	False	bool flag (set to enable)
--deepep-config	Tuned DeepEP config suitable for your own cluster. It can be either a string with JSON content or a file path.	None	Type: str
--moe-dense-tp-size	TP size for MoE dense MLP layers. This flag is useful when, with large TP size, there are errors caused by weights in MLP layers having dimension smaller than the min dimension GEMM supports.	None	Type: int
--elastic-ep-backend	Specify the collective communication backend for elastic EP. Currently supports ‘mooncake’.	none	none, mooncake
--mooncake-ib-device	The InfiniBand devices for Mooncake Backend transfer, accepts multiple comma-separated devices (e.g., –mooncake-ib-device mlx5_0,mlx5_1). Default is None, which triggers automatic device detection when Mooncake Backend is enabled.	None	Type: str

Mamba Cache

Argument	Description	Defaults	Options
--max-mamba-cache-size	The maximum size of the mamba cache.	None	Type: int
--mamba-ssm-dtype	The data type of the SSM states in mamba cache.	float32	float32, bfloat16, float16
--mamba-full-memory-ratio	The ratio of mamba state memory to full kv cache memory.	0.9	Type: float
--mamba-scheduler-strategy	The strategy to use for mamba scheduler. auto currently defaults to no_buffer. 1. no_buffer does not support overlap scheduler due to not allocating extra mamba state buffers. Branching point caching support is feasible but not implemented. 2. extra_buffer supports overlap schedule by allocating extra mamba state buffers to track mamba state for caching (mamba state usage per running req becomes 2x for non-spec; 1+(1/(2+speculative_num_draft_tokens))x for spec dec (e.g. 1.16x if speculative_num_draft_tokens==4)). 2a. extra_buffer is strictly better for non-KV-cache-bound cases; for KV-cache-bound cases, the tradeoff depends on whether enabling overlap outweighs reduced max running requests. 2b. mamba caching at radix cache branching point is strictly better than non-branch but requires kernel support (currently only FLA backend), currently only extra_buffer supports branching.	auto	auto, no_buffer, extra_buffer
--mamba-track-interval	The interval (in tokens) to track the mamba state during decode. Only used when --mamba-scheduler-strategy is extra_buffer. Must be divisible by page_size if set, and must be >= speculative_num_draft_tokens when using speculative decoding.	256	Type: int

Hierarchical cache

Argument	Description	Defaults	Options
--enable-hierarchical-cache	Enable hierarchical cache	False	bool flag (set to enable)
--hicache-ratio	The ratio of the size of host KV cache memory pool to the size of device pool.	2.0	Type: float
--hicache-size	The size of host KV cache memory pool in gigabytes, which will override the hicache_ratio if set.	0	Type: int
--hicache-write-policy	The write policy of hierarchical cache.	write_through	write_back, write_through, write_through_selective
--hicache-io-backend	The IO backend for KV cache transfer between CPU and GPU	kernel	direct, kernel, kernel_ascend
--hicache-mem-layout	The layout of host memory pool for hierarchical cache.	layer_first	layer_first, page_first, page_first_direct, page_first_kv_split, page_head
--hicache-storage-backend	The storage backend for hierarchical KV cache. Built-in backends: file, mooncake, hf3fs, nixl, aibrix. For dynamic backend, use –hicache-storage-backend-extra-config to specify: backend_name (custom name), module_path (Python module path), class_name (backend class name).	None	file, mooncake, hf3fs, nixl, aibrix, dynamic, eic
--hicache-storage-prefetch-policy	Control when prefetching from the storage backend should stop.	best_effort	best_effort, wait_complete, timeout
--hicache-storage-backend-extra-config	A dictionary in JSON string format, or a string starting with a @ followed by a config file in JSON/YAML/TOML format, containing extra configuration for the storage backend.	None	Type: str

Hierarchical sparse attention

Argument	Description	Defaults	Options
--hierarchical-sparse-attention-extra-config	A dictionary in JSON string format for hierarchical sparse attention configuration. Required fields: algorithm (str), backend (str). All other fields are algorithm-specific and passed to the algorithm constructor.	None	Type: str

LMCache

Argument	Description	Defaults	Options
--enable-lmcache	Using LMCache as an alternative hierarchical cache solution	False	bool flag (set to enable)

Ktransformers

Argument	Description	Defaults	Options
--kt-weight-path	[ktransformers parameter] The path of the quantized expert weights for amx kernel. A local folder.	None	Type: str
--kt-method	[ktransformers parameter] Quantization formats for CPU execution.	AMXINT4	Type: str
--kt-cpuinfer	[ktransformers parameter] The number of CPUInfer threads.	None	Type: int
--kt-threadpool-count	[ktransformers parameter] One-to-one with the number of NUMA nodes (one thread pool per NUMA).	2	Type: int
--kt-num-gpu-experts	[ktransformers parameter] The number of GPU experts.	None	Type: int
--kt-max-deferred-experts-per-token	[ktransformers parameter] Maximum number of experts deferred to CPU per token. All MoE layers except the final one use this value; the final layer always uses 0.	None	Type: int

Diffusion LLM

Argument	Description	Defaults	Options
--dllm-algorithm	The diffusion LLM algorithm, such as LowConfidence.	None	Type: str
--dllm-algorithm-config	The diffusion LLM algorithm configurations. Must be a YAML file.	None	Type: str

Double Sparsity

Argument	Description	Defaults	Options
--enable-double-sparsity	Enable double sparsity attention	False	bool flag (set to enable)
--ds-channel-config-path	The path of the double sparsity channel config	None	Type: str
--ds-heavy-channel-num	The number of heavy channels in double sparsity attention	32	Type: int
--ds-heavy-token-num	The number of heavy tokens in double sparsity attention	256	Type: int
--ds-heavy-channel-type	The type of heavy channels in double sparsity attention	qk	Type: str
--ds-sparse-decode-threshold	The minimum decode sequence length required before the double-sparsity backend switches from the dense fallback to the sparse decode kernel.	4096	Type: int

Offloading

Argument	Description	Defaults	Options
--cpu-offload-gb	How many GBs of RAM to reserve for CPU offloading.	0	Type: int
--offload-group-size	Number of layers per group in offloading.	-1	Type: int
--offload-num-in-group	Number of layers to be offloaded within a group.	1	Type: int
--offload-prefetch-step	Steps to prefetch in offloading.	1	Type: int
--offload-mode	Mode of offloading.	cpu	Type: str

Args for multi-item scoring

Argument	Description	Defaults	Options
--multi-item-scoring-delimiter	Delimiter token ID for multi-item scoring. Used to combine Query and Items into a single sequence: QueryItem1Item2… This enables efficient batch processing of multiple items against a single query.	None	Type: int

Optimization/debug options

Argument	Description	Defaults	Options
--disable-radix-cache	Disable RadixAttention for prefix caching.	False	bool flag (set to enable)
--cuda-graph-max-bs	Set the maximum batch size for cuda graph. It will extend the cuda graph capture batch size to this value.	None	Type: int
--cuda-graph-bs	Set the list of batch sizes for cuda graph.	None	List[int]
--disable-cuda-graph	Disable cuda graph.	False	bool flag (set to enable)
--disable-cuda-graph-padding	Disable cuda graph when padding is needed. Still uses cuda graph when padding is not needed.	False	bool flag (set to enable)
--enable-profile-cuda-graph	Enable profiling of cuda graph capture.	False	bool flag (set to enable)
--enable-cudagraph-gc	Enable garbage collection during CUDA graph capture. If disabled (default), GC is frozen during capture to speed up the process.	False	bool flag (set to enable)
--enable-layerwise-nvtx-marker	Enable layerwise NVTX profiling annotations for the model. This adds NVTX markers to every layer for detailed per-layer performance analysis with Nsight Systems.	False	bool flag (set to enable)
--enable-nccl-nvls	Enable NCCL NVLS for prefill heavy requests when available.	False	bool flag (set to enable)
--enable-symm-mem	Enable NCCL symmetric memory for fast collectives.	False	bool flag (set to enable)
--disable-flashinfer-cutlass-moe-fp4-allgather	Disables quantize before all-gather for flashinfer cutlass moe.	False	bool flag (set to enable)
--enable-tokenizer-batch-encode	Enable batch tokenization for improved performance when processing multiple text inputs. Do not use with image inputs, pre-tokenized input_ids, or input_embeds.	False	bool flag (set to enable)
--disable-tokenizer-batch-decode	Disable batch decoding when decoding multiple completions.	False	bool flag (set to enable)
--disable-outlines-disk-cache	Disable disk cache of outlines to avoid possible crashes related to file system or high concurrency.	False	bool flag (set to enable)
--disable-custom-all-reduce	Disable the custom all-reduce kernel and fall back to NCCL.	False	bool flag (set to enable)
--enable-mscclpp	Enable using mscclpp for small messages for all-reduce kernel and fall back to NCCL.	False	bool flag (set to enable)
--enable-torch-symm-mem	Enable using torch symm mem for all-reduce kernel and fall back to NCCL. Only supports CUDA device SM90 and above. SM90 supports world size 4, 6, 8. SM10 supports world size 6, 8.	False	bool flag (set to enable)
--disable-overlap-schedule	Disable the overlap scheduler, which overlaps the CPU scheduler with GPU model worker.	False	bool flag (set to enable)
--enable-mixed-chunk	Enabling mixing prefill and decode in a batch when using chunked prefill.	False	bool flag (set to enable)
--enable-dp-attention	Enabling data parallelism for attention and tensor parallelism for FFN. The dp size should be equal to the tp size. Currently DeepSeek-V2 and Qwen 2/3 MoE models are supported.	False	bool flag (set to enable)
--enable-dp-lm-head	Enable vocabulary parallel across the attention TP group to avoid all-gather across DP groups, optimizing performance under DP attention.	False	bool flag (set to enable)
--enable-two-batch-overlap	Enabling two micro batches to overlap.	False	bool flag (set to enable)
--enable-single-batch-overlap	Let computation and communication overlap within one micro batch.	False	bool flag (set to enable)
--tbo-token-distribution-threshold	The threshold of token distribution between two batches in micro-batch-overlap, determines whether to two-batch-overlap or two-chunk-overlap. Set to 0 denote disable two-chunk-overlap.	0.48	Type: float
--enable-torch-compile	Optimize the model with torch.compile. Experimental feature.	False	bool flag (set to enable)
--enable-torch-compile-debug-mode	Enable debug mode for torch compile.	False	bool flag (set to enable)
--enable-piecewise-cuda-graph	Optimize the model with piecewise cuda graph for extend/prefill only. Experimental feature.	False	bool flag (set to enable)
--piecewise-cuda-graph-tokens	Set the list of tokens when using piecewise cuda graph.	None	Type: JSON list
--piecewise-cuda-graph-compiler	Set the compiler for piecewise cuda graph. Choices are: eager, inductor.	eager	eager, inductor
--torch-compile-max-bs	Set the maximum batch size when using torch compile.	32	Type: int
--piecewise-cuda-graph-max-tokens	Set the maximum tokens when using piecewise cuda graph.	4096	Type: int
--torchao-config	Optimize the model with torchao. Experimental feature. Current choices are: int8dq, int8wo, int4wo-<group_size>, fp8wo, fp8dq-per_tensor, fp8dq-per_row	``	Type: str
--enable-nan-detection	Enable the NaN detection for debugging purposes.	False	bool flag (set to enable)
--enable-p2p-check	Enable P2P check for GPU access, otherwise the p2p access is allowed by default.	False	bool flag (set to enable)
--triton-attention-reduce-in-fp32	Cast the intermediate attention results to fp32 to avoid possible crashes related to fp16. This only affects Triton attention kernels.	False	bool flag (set to enable)
--triton-attention-num-kv-splits	The number of KV splits in flash decoding Triton kernel. Larger value is better in longer context scenarios. The default value is 8.	8	Type: int
--triton-attention-split-tile-size	The size of split KV tile in flash decoding Triton kernel. Used for deterministic inference.	None	Type: int
--num-continuous-decode-steps	Run multiple continuous decoding steps to reduce scheduling overhead. This can potentially increase throughput but may also increase time-to-first-token latency. The default value is 1, meaning only run one decoding step at a time.	1	Type: int
--delete-ckpt-after-loading	Delete the model checkpoint after loading the model.	False	bool flag (set to enable)
--enable-memory-saver	Allow saving memory using release_memory_occupation and resume_memory_occupation	False	bool flag (set to enable)
--enable-weights-cpu-backup	Save model weights to CPU memory during release_weights_occupation and resume_weights_occupation	False	bool flag (set to enable)
--enable-draft-weights-cpu-backup	Save draft model weights to CPU memory during release_weights_occupation and resume_weights_occupation	False	bool flag (set to enable)
--allow-auto-truncate	Allow automatically truncating requests that exceed the maximum input length instead of returning an error.	False	bool flag (set to enable)
--enable-custom-logit-processor	Enable users to pass custom logit processors to the server (disabled by default for security)	False	bool flag (set to enable)
--flashinfer-mla-disable-ragged	Not using ragged prefill wrapper when running flashinfer mla	False	bool flag (set to enable)
--disable-shared-experts-fusion	Disable shared experts fusion optimization for deepseek v3/r1.	False	bool flag (set to enable)
--disable-chunked-prefix-cache	Disable chunked prefix cache feature for deepseek, which should save overhead for short sequences.	False	bool flag (set to enable)
--disable-fast-image-processor	Adopt base image processor instead of fast image processor.	False	bool flag (set to enable)
--keep-mm-feature-on-device	Keep multimodal feature tensors on device after processing to save D2H copy.	False	bool flag (set to enable)
--enable-return-hidden-states	Enable returning hidden states with responses.	False	bool flag (set to enable)
--enable-return-routed-experts	Enable returning routed experts of each layer with responses.	False	bool flag (set to enable)
--scheduler-recv-interval	The interval to poll requests in scheduler. Can be set to >1 to reduce the overhead of this.	1	Type: int
--numa-node	Sets the numa node for the subprocesses. i-th element corresponds to i-th subprocess.	None	List[int]
--enable-deterministic-inference	Enable deterministic inference mode with batch invariant ops.	False	bool flag (set to enable)
--rl-on-policy-target	The training system that SGLang needs to match for true on-policy.	None	fsdp
--enable-attn-tp-input-scattered	Allow input of attention to be scattered when only using tensor parallelism, to reduce the computational load of operations such as qkv latent.	False	bool flag (set to enable)
--enable-nsa-prefill-context-parallel	Enable context parallelism used in the long sequence prefill phase of DeepSeek v3.2.	False	bool flag (set to enable)
--nsa-prefill-cp-mode	Token splitting mode for the prefill phase of DeepSeek v3.2 under context parallelism. Optional values: round-robin-split(default),in-seq-split. round-robin-split distributes tokens across ranks based on token_idx % cp_size. It supports multi-batch prefill, fused MoE, and FP8 KV cache.	in-seq-split	in-seq-split, round-robin-split
--enable-fused-qk-norm-rope	Enable fused qk normalization and rope rotary embedding.	False	bool flag (set to enable)
--enable-precise-embedding-interpolation	Enable corner alignment for resize of embeddings grid to ensure more accurate(but slower) evaluation of interpolated embedding values.	False	bool flag (set to enable)

Dynamic batch tokenizer

Argument	Description	Defaults	Options
--enable-dynamic-batch-tokenizer	Enable async dynamic batch tokenizer for improved performance when multiple requests arrive concurrently.	False	bool flag (set to enable)
--dynamic-batch-tokenizer-batch-size	[Only used if –enable-dynamic-batch-tokenizer is set] Maximum batch size for dynamic batch tokenizer.	32	Type: int
--dynamic-batch-tokenizer-batch-timeout	[Only used if –enable-dynamic-batch-tokenizer is set] Timeout in seconds for batching tokenization requests.	0.002	Type: float

Debug tensor dumps

Argument	Description	Defaults	Options
--debug-tensor-dump-output-folder	The output folder for dumping tensors.	None	Type: str
--debug-tensor-dump-layers	The layer ids to dump. Dump all layers if not specified.	None	Type: JSON list
--debug-tensor-dump-input-file	The input filename for dumping tensors	None	Type: str
--debug-tensor-dump-inject	Inject the outputs from jax as the input of every layer.	False	Type: str

PD disaggregation

Argument	Description	Defaults	Options
--disaggregation-mode	Only used for PD disaggregation. “prefill” for prefill-only server, and “decode” for decode-only server. If not specified, it is not PD disaggregated	null	null, prefill, decode
--disaggregation-transfer-backend	The backend for disaggregation transfer. Default is mooncake.	mooncake	mooncake, nixl, ascend, fake
--disaggregation-bootstrap-port	Bootstrap server port on the prefill server. Default is 8998.	8998	Type: int
--disaggregation-decode-tp	Decode tp size. If not set, it matches the tp size of the current engine. This is only set on the prefill server.	None	Type: int
--disaggregation-decode-dp	Decode dp size. If not set, it matches the dp size of the current engine. This is only set on the prefill server.	None	Type: int
--disaggregation-prefill-pp	Prefill pp size. If not set, it is default to 1. This is only set on the decode server.	1	Type: int
--disaggregation-ib-device	The InfiniBand devices for disaggregation transfer, accepts single device (e.g., –disaggregation-ib-device mlx5_0) or multiple comma-separated devices (e.g., –disaggregation-ib-device mlx5_0,mlx5_1). Default is None, which triggers automatic device detection when mooncake backend is enabled.	None	Type: str
--disaggregation-decode-enable-offload-kvcache	Enable async KV cache offloading on decode server (PD mode).	False	bool flag (set to enable)
--num-reserved-decode-tokens	Number of decode tokens that will have memory reserved when adding new request to the running batch.	512	Type: int
--disaggregation-decode-polling-interval	The interval to poll requests in decode server. Can be set to >1 to reduce the overhead of this.	1	Type: int

Encode prefill disaggregation

Argument	Description	Defaults	Options
--encoder-only	For MLLM with an encoder, launch an encoder-only server	False	bool flag (set to enable)
--language-only	For VLM, load weights for the language model only.	False	bool flag (set to enable)
--encoder-transfer-backend	The backend for encoder disaggregation transfer. Default is zmq_to_scheduler.	zmq_to_scheduler	zmq_to_scheduler, zmq_to_tokenizer, mooncake
--encoder-urls	List of encoder server urls.	[]	Type: JSON list

Custom weight loader

Argument	Description	Defaults	Options
--custom-weight-loader	The custom dataloader which used to update the model. Should be set with a valid import path, such as my_package.weight_load_func	None	List[str]
--weight-loader-disable-mmap	Disable mmap while loading weight using safetensors.	False	bool flag (set to enable)
--remote-instance-weight-loader-seed-instance-ip	The ip of the seed instance for loading weights from remote instance.	None	Type: str
--remote-instance-weight-loader-seed-instance-service-port	The service port of the seed instance for loading weights from remote instance.	None	Type: int
--remote-instance-weight-loader-send-weights-group-ports	The communication group ports for loading weights from remote instance.	None	Type: JSON list
--remote-instance-weight-loader-backend	The backend for loading weights from remote instance. Can be ‘transfer_engine’ or ‘nccl’. Default is ‘nccl’.	nccl	transfer_engine, nccl
--remote-instance-weight-loader-start-seed-via-transfer-engine	Start seed server via transfer engine backend for remote instance weight loader.	False	bool flag (set to enable)

For PD-Multiplexing

Argument	Description	Defaults	Options
--enable-pdmux	Enable PD-Multiplexing, PD running on greenctx stream.	False	bool flag (set to enable)
--pdmux-config-path	The path of the PD-Multiplexing config file.	None	Type: str
--sm-group-num	Number of sm partition groups.	8	Type: int

Configuration file support

Argument	Description	Defaults	Options
--config	Read CLI options from a config file. Must be a YAML file with configuration options.	None	Type: str

For Multi-Modal

Argument	Description	Defaults	Options
--mm-max-concurrent-calls	The max concurrent calls for async mm data processing.	32	Type: int
--mm-per-request-timeout	The timeout for each multi-modal request in seconds.	10.0	Type: int
--enable-broadcast-mm-inputs-process	Enable broadcast mm-inputs process in scheduler.	False	bool flag (set to enable)
--mm-process-config	Multimodal preprocessing config, a json config contains keys: image, video, audio.	{}	Type: JSON / Dict
--mm-enable-dp-encoder	Enabling data parallelism for mm encoder. The dp size will be set to the tp size automatically.	False	bool flag (set to enable)
--limit-mm-data-per-request	Limit the number of multimodal inputs per request. e.g. ‘{“image”: 1, “video”: 1, “audio”: 1}’	None	Type: JSON / Dict

For checkpoint decryption

Argument	Description	Defaults	Options
--decrypted-config-file	The path of the decrypted config file.	None	Type: str
--decrypted-draft-config-file	The path of the decrypted draft config file.	None	Type: str
--enable-prefix-mm-cache	Enable prefix multimodal cache. Currently only supports mm-only.	False	bool flag (set to enable)

Forward hooks

Argument	Description	Defaults	Options
--forward-hooks	JSON-formatted list of forward hook specifications. Each element must include target_modules (list of glob patterns matched against model.named_modules() names) and hook_factory (Python import path to a factory, e.g. my_package.hooks:make_hook). An optional name field is used for logging, and an optional config object is passed as a dict to the factory.	None	Type: JSON list

Deprecated arguments

Argument	Description	Defaults	Options
--enable-ep-moe	NOTE: –enable-ep-moe is deprecated. Please set --ep-size to the same value as --tp-size instead.	None	N/A
--enable-deepep-moe	NOTE: –enable-deepep-moe is deprecated. Please set --moe-a2a-backend to ‘deepep’ instead.	None	N/A
--prefill-round-robin-balance	Note: Note: –prefill-round-robin-balance is deprecated now.	None	N/A
--enable-flashinfer-cutlass-moe	NOTE: –enable-flashinfer-cutlass-moe is deprecated. Please set --moe-runner-backend to ‘flashinfer_cutlass’ instead.	None	N/A
--enable-flashinfer-cutedsl-moe	NOTE: –enable-flashinfer-cutedsl-moe is deprecated. Please set --moe-runner-backend to ‘flashinfer_cutedsl’ instead.	None	N/A
--enable-flashinfer-trtllm-moe	NOTE: –enable-flashinfer-trtllm-moe is deprecated. Please set --moe-runner-backend to ‘flashinfer_trtllm’ instead.	None	N/A
--enable-triton-kernel-moe	NOTE: –enable-triton-kernel-moe is deprecated. Please set --moe-runner-backend to ‘triton_kernel’ instead.	None	N/A
--enable-flashinfer-mxfp4-moe	NOTE: –enable-flashinfer-mxfp4-moe is deprecated. Please set --moe-runner-backend to ‘flashinfer_mxfp4’ instead.	None	N/A
--crash-on-nan	Crash the server on nan logprobs.	False	Type: str
--hybrid-kvcache-ratio	Mix ratio in [0,1] between uniform and hybrid kv buffers (0.0 = pure uniform: swa_size / full_size = 1)(1.0 = pure hybrid: swa_size / full_size = local_attention_size / context_length)	None	Optional[float]
--load-watch-interval	The interval of load watching in seconds.	0.1	Type: float
--nsa-prefill	Choose the NSA backend for the prefill stage (overrides --attention-backend when running DeepSeek NSA-style attention).	flashmla_sparse	flashmla_sparse, flashmla_decode, fa3, tilelang, aiter
--nsa-decode	Choose the NSA backend for the decode stage when running DeepSeek NSA-style attention. Overrides --attention-backend for decoding.	flashmla_kv	flashmla_prefill, flashmla_kv, fa3, tilelang, aiter