[AI] Developing Native Multimodal Agents with Qwen3.5 VLM Using NVIDIA GPU-Accelerated Endpoints

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The current AI technology is rapidly evolving, and the development of multimodal agents is gaining attention. Recently, NVIDIA provided a guide for developing native multimodal agents using Qwen3.5 VLM with GPU-accelerated endpoints.

Introduction to Qwen3.5 VLM

Qwen3.5 VLM is a multimodal learning model developed by NVIDIA. This model supports various modalities such as natural language processing, computer vision, and audio processing. Using Qwen3.5 VLM, developers can easily develop multimodal agents.

Developing Native Multimodal Agents

Native multimodal agents are agents that support various modalities. To develop such agents, developers need to combine different technologies. NVIDIA's guide provides a method for developing native multimodal agents using Qwen3.5 VLM.

NVIDIA GPU-Accelerated Endpoints

NVIDIA GPU-accelerated endpoints support developers in using GPUs to develop agents. This endpoint enables developers to quickly develop and test agents.

The Future of Multimodal Agents

Multimodal agents can be used in various fields. For example, in the medical field, multimodal agents can be used to analyze a patient's condition. Additionally, in the customer service field, multimodal agents can be used to answer customer questions.

Conclusion

Using NVIDIA's Qwen3.5 VLM and GPU-accelerated endpoints, developers can develop native multimodal agents. This technology can be used in various fields and supports developers in easily developing agents. What does the future hold for multimodal agents?