add sglang to multiple sections

README.md

@@ -169,6 +169,7 @@ Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated sys
 - [PyTorch](https://github.com/nvidia/cosmos3)
 - [vLLM-Omni](https://github.com/vllm-project/vllm-omni)
 - [Hugging Face Diffusers](https://huggingface.co/docs/diffusers/en/index)
+- [SGLang](https://sgl-project.github.io/)
 
 **Supported Hardware Microarchitecture Compatibility:**
 
@@ -918,6 +919,12 @@ Example output:
 
 <video controls width="1280" height="720" src="https://huggingface.co/nvidia/Cosmos3-Nano/resolve/main/assets/example_t2v_diffusers_output.mp4"></video>
 
+### SGLang
+
+[SGLang Diffusion](https://sgl-project.github.io/diffusion) can serve `nvidia/Cosmos3-Nano` through OpenAI-compatible image and video generation endpoints.
+
+For complete serving instructions and request examples, see the [Cosmos3 SGLang cookbook](https://lmsysorg.mintlify.app/cookbook/diffusion/Cosmos/Cosmos3).
+
 ## Limitations
 
 Cosmos3 may produce imperfect outputs in challenging scenarios. Generation artifacts include temporal inconsistency, unstable camera or object motion, imprecise physical interactions, inaccurate audio-video synchronization, and action-state drift — especially in long-horizon or high-resolution outputs. Reasoning may also be incorrect: object states, causal relationships, spatial geometry, temporal ordering, agent intent, and future outcomes can be misinferred, and complex or long-context inputs may yield hallucinated entities, inconsistent interpretations, or implausible predictions. Because the model lacks an explicit physics simulator, 3D geometry, 4D space-time evolution, object permanence, contact dynamics, and physical laws are only approximated — producing artifacts such as disappearing or morphing objects, unrealistic collisions, and physically implausible motions. Quality further degrades in out-of-distribution environments, safety-critical edge cases, and domains underrepresented in training.
@@ -988,7 +995,7 @@ SGLang accepts Cosmos 3 request options including `max_sequence_length`, `flow_s
 
 ## Inference
 
-**Acceleration Engine:** [PyTorch](https://pytorch.org/), [vLLM](https://github.com/vllm-project/vllm), [vLLM-Omni](https://github.com/vllm-project/vllm-omni), [Hugging Face Diffusers](https://github.com/huggingface/diffusers)
+**Acceleration Engine:** [PyTorch](https://pytorch.org/), [vLLM](https://github.com/vllm-project/vllm), [vLLM-Omni](https://github.com/vllm-project/vllm-omni), [Hugging Face Diffusers](https://github.com/huggingface/diffusers), [SGLang](https://sgl-project.github.io/), [SGLang Diffusion](https://sgl-project.github.io/diffusion)
 
 **Test Hardware:** GB200 and H100