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# Diffusion Language Models
Diffusion language models have shown promise for non-autoregressive text generation with parallel decoding capabilities. Unlike auto-regressive language models, different diffusion language models require different decoding strategies.
## Example Launch Command
SGLang supports different DLLM algorithms such as `LowConfidence` and `JointThreshold`.
```shell
python3 -m sglang.launch_server \
--model-path inclusionAI/LLaDA2.0-mini \ # example HF/local path
--dllm-algorithm LowConfidence \
--dllm-algorithm-config ./config.yaml \ # Optional. Uses the algorithm's default if not set.
--host 0.0.0.0 \
--port 30000
```
## Example Configuration File
Depending on the algorithm selected, the configuration parameters vary.
LowConfidence Config:
```yaml
# Confidence threshold for accepting predicted tokens
# - Higher values: More conservative, better quality but slower
# - Lower values: More aggressive, faster but potentially lower quality
# Range: 0.0 - 1.0
threshold: 0.95
# Default: 32, for LLaDA2MoeModelLM
block_size: 32
```
JointThreshold Config:
```yaml
# Decoding threshold for Mask-to-Token (M2T) phase
# - Higher values: More conservative, better quality but slower
# - Lower values: More aggressive, faster but potentially lower quality
# Range: 0.0 - 1.0
threshold: 0.5
# Decoding threshold for Token-to-Token (T2T) phase
# Range: 0.0 - 1.0
# Setting to 0.0 allows full editing (recommended for most cases).
edit_threshold: 0.0
# Max extra T2T steps after all masks are removed. Prevents infinite loops.
max_post_edit_steps: 16
# 2-gram repetition penalty (default 0).
# An empirical value of 3 is often sufficient to mitigate most repetitions.
penalty_lambda: 0
```
## Example Client Code Snippet
Just like other supported models, diffusion language models can be used via the REST API or Python client.
Python client example for making a generation request to the launched server:
```python
import sglang as sgl
def main():
llm = sgl.Engine(model_path="inclusionAI/LLaDA2.0-mini",
dllm_algorithm="LowConfidence",
max_running_requests=1,
trust_remote_code=True)
prompts = [
"<role>SYSTEM</role>detailed thinking off<|role_end|><role>HUMAN</role> Write a brief introduction of the great wall <|role_end|><role>ASSISTANT</role>"
]
sampling_params = {
"temperature": 0,
"max_new_tokens": 1024,
}
outputs = llm.generate(prompts, sampling_params)
print(outputs)
if __name__ == '__main__':
main()
```
Curl example for making a generation request to the launched server:
```bash
curl -X POST "http://127.0.0.1:30000/generate" \
-H "Content-Type: application/json" \
-d '{
"text": [
"<role>SYSTEM</role>detailed thinking off<|role_end|><role>HUMAN</role> Write the number from 1 to 128 <|role_end|><role>ASSISTANT</role>",
"<role>SYSTEM</role>detailed thinking off<|role_end|><role>HUMAN</role> Write a brief introduction of the great wall <|role_end|><role>ASSISTANT</role>"
],
"stream": true,
"sampling_params": {
"temperature": 0,
"max_new_tokens": 1024
}
}'
```
## Supported Models
Below the supported models are summarized in a table.
| Model Family | Example Model | Description |
| -------------------------- | ---------------------------- | ---------------------------------------------------------------------------------------------------- |
| **LLaDA2.0 (mini, flash)** | `inclusionAI/LLaDA2.0-flash` | LLaDA2.0-flash is a diffusion language model featuring a 100B Mixture-of-Experts (MoE) architecture. |
| **SDAR (JetLM)** | `JetLM/SDAR-8B-Chat` | SDAR series diffusion language model (Chat), dense architecture. |
| **SDAR (JetLM)** | `JetLM/SDAR-30B-A3B-Chat` | SDAR series diffusion language model (Chat), MoE architecture. |

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# Large Language Models
These models accept text input and produce text output (e.g., chat completions). They are primarily large language models (LLMs), some with mixture-of-experts (MoE) architectures for scaling.
## Example launch Command
```shell
python3 -m sglang.launch_server \
--model-path meta-llama/Llama-3.2-1B-Instruct \ # example HF/local path
--host 0.0.0.0 \
--port 30000 \
```
## Supported models
Below the supported models are summarized in a table.
If you are unsure if a specific architecture is implemented, you can search for it via GitHub. For example, to search for `Qwen3ForCausalLM`, use the expression:
```
repo:sgl-project/sglang path:/^python\/sglang\/srt\/models\// Qwen3ForCausalLM
```
in the GitHub search bar.
| Model Family (Variants) | Example HuggingFace Identifier | Description |
|-------------------------------------|--------------------------------------------------|----------------------------------------------------------------------------------------|
| **DeepSeek** (v1, v2, v3/R1) | `deepseek-ai/DeepSeek-R1` | Series of advanced reasoning-optimized models (including a 671B MoE) trained with reinforcement learning; top performance on complex reasoning, math, and code tasks. [SGLang provides Deepseek v3/R1 model-specific optimizations](../../basic_usage/deepseek_v3.md) and [Reasoning Parser](../../advanced_features/separate_reasoning.ipynb)|
| **Kimi K2** (Thinking, Instruct) | `moonshotai/Kimi-K2-Instruct` | Moonshot AI's 1 trillion parameter MoE model (32B active) with 128K–256K context; state-of-the-art agentic intelligence with stable long-horizon agency across 200–300 sequential tool calls. Features MLA attention and native INT4 quantization. [See Reasoning Parser docs](../../advanced_features/separate_reasoning.ipynb)|
| **Kimi Linear** (48B-A3B) | `moonshotai/Kimi-Linear-48B-A3B-Instruct` | Moonshot AI's hybrid linear attention model (48B total, 3B active) with 1M token context; features Kimi Delta Attention (KDA) for up to 6× faster decoding and 75% KV cache reduction vs full attention. |
| **GPT-OSS** | `openai/gpt-oss-20b`, `openai/gpt-oss-120b` | OpenAI’s latest GPT-OSS series for complex reasoning, agentic tasks, and versatile developer use cases.|
| **Qwen** (3.5, 3, 3MoE, 3Next, 2.5, 2 series) | `Qwen/Qwen3.5-397B-A17B`, `Qwen/Qwen3-0.6B`, `Qwen/Qwen3-30B-A3B`, `Qwen/Qwen3-Next-80B-A3B-Instruct` | Alibaba’s latest Qwen3 series for complex reasoning, language understanding, and generation tasks; Support for MoE variants along with previous generation 2.5, 2, etc. [SGLang provides Qwen3 specific reasoning parser](../../advanced_features/separate_reasoning.ipynb)|
| **Llama** (2, 3.x, 4 series) | `meta-llama/Llama-4-Scout-17B-16E-Instruct` | Meta's open LLM series, spanning 7B to 400B parameters (Llama 2, 3, and new Llama 4) with well-recognized performance. [SGLang provides Llama-4 model-specific optimizations](../../basic_usage/llama4.md) |
| **Mistral** (Mixtral, NeMo, Small3) | `mistralai/Mistral-7B-Instruct-v0.2` | Open 7B LLM by Mistral AI with strong performance; extended into MoE (“Mixtral”) and NeMo Megatron variants for larger scale. |
| **Gemma** (v1, v2, v3) | `google/gemma-3-1b-it` | Google’s family of efficient multilingual models (1B–27B); Gemma 3 offers a 128K context window, and its larger (4B+) variants support vision input. |
| **Phi** (Phi-1.5, Phi-2, Phi-3, Phi-4, Phi-MoE series) | `microsoft/Phi-4-multimodal-instruct`, `microsoft/Phi-3.5-MoE-instruct` | Microsoft’s Phi family of small models (1.3B–5.6B); Phi-4-multimodal (5.6B) processes text, images, and speech, Phi-4-mini is a high-accuracy text model and Phi-3.5-MoE is a mixture-of-experts model. |
| **MiniCPM** (v3, 4B) | `openbmb/MiniCPM3-4B` | OpenBMB’s series of compact LLMs for edge devices; MiniCPM 3 (4B) achieves GPT-3.5-level results in text tasks. |
| **OLMo** (2, 3) | `allenai/OLMo-3-1125-32B`, `allenai/OLMo-3-32B-Think`, `allenai/OLMo-2-1124-7B-Instruct` | Allen AI’s series of Open Language Models designed to enable the science of language models. |
| **OLMoE** (Open MoE) | `allenai/OLMoE-1B-7B-0924` | Allen AI’s open Mixture-of-Experts model (7B total, 1B active parameters) delivering state-of-the-art results with sparse expert activation. |
| **MiniMax-M2** (M2, M2.1, M2.5) | `MiniMaxAI/MiniMax-M2.5`, `MiniMaxAI/MiniMax-M2.1`, `MiniMaxAI/MiniMax-M2` | MiniMax's SOTA LLM for coding & agentic workflows. |
| **StableLM** (3B, 7B) | `stabilityai/stablelm-tuned-alpha-7b` | StabilityAI’s early open-source LLM (3B & 7B) for general text generation; a demonstration model with basic instruction-following ability. |
| **Command-(R,A)** (Cohere) | `CohereLabs/c4ai-command-r-v01`, `CohereLabs/c4ai-command-r7b-12-2024`, `CohereLabs/c4ai-command-a-03-2025` | Cohere’s open conversational LLM (Command series) optimized for long context, retrieval-augmented generation, and tool use. |
| **DBRX** (Databricks) | `databricks/dbrx-instruct` | Databricks’ 132B-parameter MoE model (36B active) trained on 12T tokens; competes with GPT-3.5 quality as a fully open foundation model. |
| **Grok** (xAI) | `xai-org/grok-1` | xAI’s grok-1 model known for vast size(314B parameters) and high quality; integrated in SGLang for high-performance inference. |
| **ChatGLM** (GLM-130B family) | `THUDM/chatglm2-6b` | Zhipu AI’s bilingual chat model (6B) excelling at Chinese-English dialogue; fine-tuned for conversational quality and alignment. |
| **InternLM 2** (7B, 20B) | `internlm/internlm2-7b` | Next-gen InternLM (7B and 20B) from SenseTime, offering strong reasoning and ultra-long context support (up to 200K tokens). |
| **ExaONE 3** (Korean-English) | `LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct` | LG AI Research’s Korean-English model (7.8B) trained on 8T tokens; provides high-quality bilingual understanding and generation. |
| **Baichuan 2** (7B, 13B) | `baichuan-inc/Baichuan2-13B-Chat` | BaichuanAI’s second-generation Chinese-English LLM (7B/13B) with improved performance and an open commercial license. |
| **XVERSE** (MoE) | `xverse/XVERSE-MoE-A36B` | Yuanxiang’s open MoE LLM (XVERSE-MoE-A36B: 255B total, 36B active) supporting ~40 languages; delivers 100B+ dense-level performance via expert routing. |
| **SmolLM** (135M–1.7B) | `HuggingFaceTB/SmolLM-1.7B` | Hugging Face’s ultra-small LLM series (135M–1.7B params) offering surprisingly strong results, enabling advanced AI on mobile/edge devices. |
| **GLM-4** (Multilingual 9B) | `ZhipuAI/glm-4-9b-chat` | Zhipu’s GLM-4 series (up to 9B parameters) – open multilingual models with support for 1M-token context and even a 5.6B multimodal variant (Phi-4V). |
| **MiMo** (7B series) | `XiaomiMiMo/MiMo-7B-RL` | Xiaomi's reasoning-optimized model series, leverages Multiple-Token Prediction for faster inference. |
| **ERNIE-4.5** (4.5, 4.5MoE series) | `baidu/ERNIE-4.5-21B-A3B-PT` | Baidu's ERNIE-4.5 series which consists of MoE with 47B and 3B active parameters, with the largest model having 424B total parameters, as well as a 0.3B dense model. |
| **Arcee AFM-4.5B** | `arcee-ai/AFM-4.5B-Base` | Arcee's foundational model series for real world reliability and edge deployments. |
| **Persimmon** (8B) | `adept/persimmon-8b-chat` | Adept’s open 8B model with a 16K context window and fast inference; trained for broad usability and licensed under Apache 2.0. |
| **Solar** (10.7B) | `upstage/SOLAR-10.7B-Instruct-v1.0` | Upstage's 10.7B parameter model, optimized for instruction-following tasks. This architecture incorporates a depth-up scaling methodology, enhancing model performance. |
| **Tele FLM** (52B-1T) | `CofeAI/Tele-FLM` | BAAI & TeleAI's multilingual model, available in 52-billion and 1-trillion parameter variants. It is a decoder-only transformer trained on ~2T tokens |
| **Ling** (16.8B–290B) | `inclusionAI/Ling-lite`, `inclusionAI/Ling-plus` | InclusionAI’s open MoE models. Ling-Lite has 16.8B total / 2.75B active parameters, and Ling-Plus has 290B total / 28.8B active parameters. They are designed for high performance on NLP and complex reasoning tasks. |
| **Granite 3.0, 3.1** (IBM) | `ibm-granite/granite-3.1-8b-instruct` | IBM's open dense foundation models optimized for reasoning, code, and business AI use cases. Integrated with Red Hat and watsonx systems. |
| **Granite 3.0 MoE** (IBM) | `ibm-granite/granite-3.0-3b-a800m-instruct` | IBM’s Mixture-of-Experts models offering strong performance with cost-efficiency. MoE expert routing designed for enterprise deployment at scale. |
| **GPT-J** (6B) | `EleutherAI/gpt-j-6b` | EleutherAI's GPT-2-like causal language model (6B) trained on the [Pile](https://pile.eleuther.ai/) dataset. |
| **Orion** (14B) | `OrionStarAI/Orion-14B-Base` | A series of open-source multilingual large language models by OrionStarAI, pretrained on a 2.5T token multilingual corpus including Chinese, English, Japanese, Korean, etc, and it exhibits superior performance in these languages. |
| **Llama Nemotron Super** (v1, v1.5, NVIDIA) | `nvidia/Llama-3_3-Nemotron-Super-49B-v1`, `nvidia/Llama-3_3-Nemotron-Super-49B-v1_5` | The [NVIDIA Nemotron](https://www.nvidia.com/en-us/ai-data-science/foundation-models/nemotron/) family of multimodal models provides state-of-the-art reasoning models specifically designed for enterprise-ready AI agents. |
| **Llama Nemotron Ultra** (v1, NVIDIA) | `nvidia/Llama-3_1-Nemotron-Ultra-253B-v1` | The [NVIDIA Nemotron](https://www.nvidia.com/en-us/ai-data-science/foundation-models/nemotron/) family of multimodal models provides state-of-the-art reasoning models specifically designed for enterprise-ready AI agents. |
| **NVIDIA Nemotron Nano 2.0** | `nvidia/NVIDIA-Nemotron-Nano-9B-v2` | The [NVIDIA Nemotron](https://www.nvidia.com/en-us/ai-data-science/foundation-models/nemotron/) family of multimodal models provides state-of-the-art reasoning models specifically designed for enterprise-ready AI agents. `Nemotron-Nano-9B-v2` is a hybrid Mamba-Transformer language model designed to increase throughput for reasoning workloads while achieving state-of-the-art accuracy compared to similarly-sized models. |
| **NVIDIA Nemotron 3 Super** (NVIDIA) | `nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4` | The [NVIDIA Nemotron](https://www.nvidia.com/en-us/ai-data-science/foundation-models/nemotron/) 3 Super is a 120B-parameter MoE model (12B active) delivering high-quality reasoning and generation for enterprise AI agents. |
| **NVIDIA Nemotron 3 Nano** (NVIDIA) | `nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16` | The [NVIDIA Nemotron](https://www.nvidia.com/en-us/ai-data-science/foundation-models/nemotron/) 3 Nano is a compact model designed for efficient edge and enterprise deployment with strong reasoning capabilities. |
| **StarCoder2** (3B-15B) | `bigcode/starcoder2-7b` | StarCoder2 is a family of open large language models (LLMs) specialized for code generation and understanding. It is the successor to StarCoder, jointly developed by the BigCode project (a collaboration between Hugging Face, ServiceNow Research, and other contributors). |
| **Jet-Nemotron** | `jet-ai/Jet-Nemotron-2B` | Jet-Nemotron is a new family of hybrid-architecture language models that surpass state-of-the-art open-source full-attention language models, while achieving significant efficiency gains. |
| **Trinity** (Nano, Mini) | `arcee-ai/Trinity-Mini` | Arcee's foundational MoE Trinity family of models, open weights under Apache 2.0. |
| **LFM2** (350M, 1.2B) | `LiquidAI/LFM2.5-1.2B-Instruct` | Liquid AI's hybrid attention + short convolution language model. |
| **LFM2-MoE** (8B-A1B, 24B-A2B) | `LiquidAI/LFM2-8B-A1B` | Liquid AI's Mixture-of-Experts variant with sigmoid routing and top-k expert selection. |
| **Falcon-H1** (0.5B–34B) | `tiiuae/Falcon-H1-34B-Instruct` | TII's hybrid Mamba-Transformer architecture combining attention and state-space models for efficient long-context inference. |
| **Hunyuan-Large** (389B, MoE) | `tencent/Tencent-Hunyuan-Large` | Tencent's open-source MoE model with 389B total / 52B active parameters, featuring Cross-Layer Attention (CLA) for improved efficiency. |
| **IBM Granite 4.0 (Hybrid, Dense)** | `ibm-granite/granite-4.0-h-micro`, `ibm-granite/granite-4.0-micro` | IBM Granite 4.0 micro models: hybrid Mamba–MoE (`h-micro`) and dense (`micro`) variants. Enterprise-focused reasoning models |
| **Sarvam 2** (30B-A2B, 105B-A10B) | `sarvamai/sarvam-2` | Sarvam's Mixture-of-Experts models. The 105B variant uses MLA (Multi-head Latent Attention) and the 30B variant uses GQA, both with 128 routed experts. |

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Text Generation
===============
Models for generating text from text or multimodal inputs.
.. toctree::
:maxdepth: 1
generative_models.md
multimodal_language_models.md
diffusion_language_models.md

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# Multimodal Language Models
These models accept multi-modal inputs (e.g., images and text) and generate text output. They augment language models with multimodal encoders.
## Example launch Command
```shell
python3 -m sglang.launch_server \
--model-path meta-llama/Llama-3.2-11B-Vision-Instruct \ # example HF/local path
--host 0.0.0.0 \
--port 30000 \
```
> See the [OpenAI APIs section](https://docs.sglang.io/basic_usage/openai_api_vision.html) for how to send multimodal requests.
## Supported models
Below the supported models are summarized in a table.
If you are unsure if a specific architecture is implemented, you can search for it via GitHub. For example, to search for `Qwen2_5_VLForConditionalGeneration`, use the expression:
```
repo:sgl-project/sglang path:/^python\/sglang\/srt\/models\// Qwen2_5_VLForConditionalGeneration
```
in the GitHub search bar.
| Model Family (Variants) | Example HuggingFace Identifier | Description | Notes |
|----------------------------|--------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------|
| **Qwen-VL** | `Qwen/Qwen3-VL-235B-A22B-Instruct` | Alibaba's vision-language extension of Qwen; for example, Qwen2.5-VL (7B and larger variants) can analyze and converse about image content. | |
| **DeepSeek-VL2** | `deepseek-ai/deepseek-vl2` | Vision-language variant of DeepSeek (with a dedicated image processor), enabling advanced multimodal reasoning on image and text inputs. | |
| **DeepSeek-OCR / OCR-2** | `deepseek-ai/DeepSeek-OCR-2` | OCR-focused DeepSeek models for document understanding and text extraction. | Use `--trust-remote-code`. |
| **Janus-Pro** (1B, 7B) | `deepseek-ai/Janus-Pro-7B` | DeepSeek's open-source multimodal model capable of both image understanding and generation. Janus-Pro employs a decoupled architecture for separate visual encoding paths, enhancing performance in both tasks. | |
| **MiniCPM-V / MiniCPM-o** | `openbmb/MiniCPM-V-2_6` | MiniCPM-V (2.6, ~8B) supports image inputs, and MiniCPM-o adds audio/video; these multimodal LLMs are optimized for end-side deployment on mobile/edge devices. | |
| **Llama 3.2 Vision** (11B) | `meta-llama/Llama-3.2-11B-Vision-Instruct` | Vision-enabled variant of Llama 3 (11B) that accepts image inputs for visual question answering and other multimodal tasks. | |
| **LLaVA** (v1.5 & v1.6) | *e.g.* `liuhaotian/llava-v1.5-13b` | Open vision-chat models that add an image encoder to LLaMA/Vicuna (e.g. LLaMA2 13B) for following multimodal instruction prompts. | |
| **LLaVA-NeXT** (8B, 72B) | `lmms-lab/llava-next-72b` | Improved LLaVA models (with an 8B Llama3 version and a 72B version) offering enhanced visual instruction-following and accuracy on multimodal benchmarks. | |
| **LLaVA-OneVision** | `lmms-lab/llava-onevision-qwen2-7b-ov` | Enhanced LLaVA variant integrating Qwen as the backbone; supports multiple images (and even video frames) as inputs via an OpenAI Vision API-compatible format. | |
| **Gemma 3 (Multimodal)** | `google/gemma-3-4b-it` | Gemma 3's larger models (4B, 12B, 27B) accept images (each image encoded as 256 tokens) alongside text in a combined 128K-token context. | |
| **Kimi-VL** (A3B) | `moonshotai/Kimi-VL-A3B-Instruct` | Kimi-VL is a multimodal model that can understand and generate text from images. | |
| **Mistral-Small-3.1-24B** | `mistralai/Mistral-Small-3.1-24B-Instruct-2503` | Mistral 3.1 is a multimodal model that can generate text from text or images input. It also supports tool calling and structured output. | |
| **Phi-4-multimodal-instruct** | `microsoft/Phi-4-multimodal-instruct` | Phi-4-multimodal-instruct is the multimodal variant of the Phi-4-mini model, enhanced with LoRA for improved multimodal capabilities. It supports text, vision and audio modalities in SGLang. | |
| **MiMo-VL** (7B) | `XiaomiMiMo/MiMo-VL-7B-RL` | Xiaomi's compact yet powerful vision-language model featuring a native resolution ViT encoder for fine-grained visual details, an MLP projector for cross-modal alignment, and the MiMo-7B language model optimized for complex reasoning tasks. | |
| **GLM-4.5V** (106B) / **GLM-4.1V**(9B) | `zai-org/GLM-4.5V` | GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning | Use `--chat-template glm-4v` |
| **GLM-OCR** | `zai-org/GLM-OCR` | GLM-OCR: A fast and accurate general OCR model | |
| **DotsVLM** (General/OCR) | `rednote-hilab/dots.vlm1.inst` | RedNote's vision-language model built on a 1.2B vision encoder and DeepSeek V3 LLM, featuring NaViT vision encoder trained from scratch with dynamic resolution support and enhanced OCR capabilities through structured image data training. | |
| **DotsVLM-OCR** | `rednote-hilab/dots.ocr` | Specialized OCR variant of DotsVLM optimized for optical character recognition tasks with enhanced text extraction and document understanding capabilities. | Don't use `--trust-remote-code` |
| **NVILA** (8B, 15B, Lite-2B, Lite-8B, Lite-15B) | `Efficient-Large-Model/NVILA-8B` | `chatml` | NVILA explores the full stack efficiency of multi-modal design, achieving cheaper training, faster deployment and better performance. |
| **NVIDIA Nemotron Nano 2.0 VL** | `nvidia/NVIDIA-Nemotron-Nano-12B-v2-VL-BF16` | NVIDIA Nemotron Nano v2 VL enables multi-image reasoning and video understanding, along with strong document intelligence, visual Q&A and summarization capabilities. It builds on Nemotron Nano V2, a hybrid Mamba-Transformer LLM, in order to achieve higher inference throughput in long document and video scenarios. | Use `--trust-remote-code`. You may need to adjust `--max-mamba-cache-size` [default is 512] to fit memory constraints. |
| **Ernie4.5-VL** | `baidu/ERNIE-4.5-VL-28B-A3B-PT` | Baidu's vision-language models(28B,424B). Support image and video comprehension, and also support thinking. | |
| **JetVLM** | | JetVLM is an vision-language model designed for high-performance multimodal understanding and generation tasks built upon Jet-Nemotron. | Coming soon |
| **Step3-VL** (10B) | `stepfun-ai/Step3-VL-10B` | StepFun's lightweight open-source 10B parameter VLM for multimodal intelligence, excelling in visual perception, complex reasoning, and human alignment. | |
| **Qwen3-Omni** | `Qwen/Qwen3-Omni-30B-A3B-Instruct` | Alibaba's omni-modal MoE model. Currently supports the **Thinker** component (multimodal understanding for text, images, audio, and video), while the **Talker** component (audio generation) is not yet supported. | |
| **LFM2-VL** | `LiquidAI/LFM2.5-VL-1.6B` | Liquid AI's vision-language model combining a SigLip2 vision encoder (NaFlex variable-resolution) with the LFM2 hybrid attention + short convolution language model. Supports multi-image inputs. | |
## Video Input Support
SGLang supports video input for Vision-Language Models (VLMs), enabling temporal reasoning tasks such as video question answering, captioning, and holistic scene understanding. Video clips are decoded, key frames are sampled, and the resulting tensors are batched together with the text prompt, allowing multimodal inference to integrate visual and linguistic context.
| Model Family | Example Identifier | Video notes |
|--------------|--------------------|-------------|
| **Qwen-VL** (Qwen2-VL, Qwen2.5-VL, Qwen3-VL, Qwen3-Omni) | `Qwen/Qwen3-VL-235B-A22B-Instruct` | The processor gathers `video_data`, runs Qwen's frame sampler, and merges the resulting features with text tokens before inference. |
| **GLM-4v** (4.5V, 4.1V, MOE) | `zai-org/GLM-4.5V` | Video clips are read with Decord, converted to tensors, and passed to the model alongside metadata for rotary-position handling. |
| **NVILA** (Full & Lite) | `Efficient-Large-Model/NVILA-8B` | The runtime samples eight frames per clip and attaches them to the multimodal request when `video_data` is present. |
| **LLaVA video variants** (LLaVA-NeXT-Video, LLaVA-OneVision) | `lmms-lab/LLaVA-NeXT-Video-7B` | The processor routes video prompts to the LlavaVid video-enabled architecture, and the provided example shows how to query it with `sgl.video(...)` clips. |
| **NVIDIA Nemotron Nano 2.0 VL** | `nvidia/NVIDIA-Nemotron-Nano-12B-v2-VL-BF16` | The processor samples at 2 FPS, at a max of 128 frames, as per model training. The model uses [EVS](../../../python/sglang/srt/multimodal/evs/README.md), a pruning method that removes redundant tokens from video embeddings. By default `video_pruning_rate=0.7`. Change this by providing: `--json-model-override-args '{"video_pruning_rate": 0.0}'` to disable EVS, for example. |
| **JetVLM** | | The runtime samples eight frames per clip and attaches them to the multimodal request when `video_data` is present. |
Use `sgl.video(path, num_frames)` when building prompts to attach clips from your SGLang programs.
Example OpenAI-compatible request that sends a video clip:
```python
import requests
url = "http://localhost:30000/v1/chat/completions"
data = {
"model": "Qwen/Qwen3-VL-30B-A3B-Instruct",
"messages": [
{
"role": "user",
"content": [
{"type": "text", "text": "What’s happening in this video?"},
{
"type": "video_url",
"video_url": {
"url": "https://github.com/sgl-project/sgl-test-files/raw/refs/heads/main/videos/jobs_presenting_ipod.mp4"
},
},
],
}
],
"max_tokens": 300,
}
response = requests.post(url, json=data)
print(response.text)
```
## Usage Notes
### Performance Optimization
For multimodal models, you can use the `--keep-mm-feature-on-device` flag to optimize for latency at the cost of increased GPU memory usage:
- **Default behavior**: Multimodal feature tensors are moved to CPU after processing to save GPU memory
- **With `--keep-mm-feature-on-device`**: Feature tensors remain on GPU, reducing device-to-host copy overhead and improving latency, but consuming more GPU memory
Use this flag when you have sufficient GPU memory and want to minimize latency for multimodal inference.
### Multimodal Inputs Limitation
- **Use `--mm-process-config '{"image":{"max_pixels":1048576},"video":{"fps":3,"max_pixels":602112,"max_frames":60}}'`**: To set `image`, `video`, and `audio` input limits.
This can reduce GPU memory usage, improve inference speed, and help to avoid OOM, but may impact model performance, thus set a proper value based on your specific use case. Currently, only `qwen_vl` supports this config. Please refer to [qwen_vl processor](https://github.com/sgl-project/sglang/blob/main/python/sglang/srt/multimodal/processors/qwen_vl.py) for understanding the meaning of each parameter.
### Bidirectional Attention in Multimodal Model Serving
**Note for serving the Gemma-3 multimodal model**:
As mentioned in [Welcome Gemma 3: Google's all new multimodal, multilingual, long context open LLM
](https://huggingface.co/blog/gemma3#multimodality), Gemma-3 employs bidirectional attention between image tokens during the prefill phase. Currently, SGLang only supports bidirectional attention when using the Triton Attention Backend. Note, however, that SGLang's current bidirectional attention implementation is incompatible with both CUDA Graph and Chunked Prefill.
To enable bidirectional attention, you can use the `TritonAttnBackend` while disabling CUDA Graph and Chunked Prefill. Example launch command:
```shell
python -m sglang.launch_server \
--model-path google/gemma-3-4b-it \
--host 0.0.0.0 --port 30000 \
--enable-multimodal \
--dtype bfloat16 --triton-attention-reduce-in-fp32 \
--attention-backend triton \ # Use Triton attention backend
--disable-cuda-graph \ # Disable Cuda Graph
--chunked-prefill-size -1 # Disable Chunked Prefill
```
If higher serving performance is required and a certain degree of accuracy loss is acceptable, you may choose to use other attention backends, and you can also enable features like CUDA Graph and Chunked Prefill for better performance, but note that the model will fall back to using causal attention instead of bidirectional attention.