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Isha Salian@NVIDIA Blog
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Nvidia is pushing the boundaries of artificial intelligence with a focus on multimodal generative AI and tools to enhance AI model integration. Nvidia's research division is actively involved in advancing AI across various sectors, underscored by the presentation of over 70 research papers at the International Conference on Learning Representations (ICLR) in Singapore. These papers cover a diverse range of topics including generative AI, robotics, autonomous driving, and healthcare, demonstrating Nvidia's commitment to innovation across the AI spectrum. Bryan Catanzaro, vice president of applied deep learning research at NVIDIA, emphasized the company's aim to accelerate every level of the computing stack to amplify the impact and utility of AI across industries.
Research efforts at Nvidia are not limited to theoretical advancements. The company is also developing tools that streamline the integration of AI models into real-world applications. One notable example is the work being done with NVIDIA NIM microservices, which are being leveraged by researchers at the University College London (UCL) Deciding, Acting, and Reasoning with Knowledge (DARK) Lab to benchmark agentic LLM and VLM reasoning for gaming. These microservices simplify the deployment and scaling of AI models, enabling researchers to efficiently handle workloads of any size and customize models for specific needs.
Nvidia's NIM microservices are designed to redefine how researchers and developers deploy and scale AI models, offering a streamlined approach to harnessing the power of GPUs. These microservices simplify the process of running AI inference workloads by providing pre-optimized engines such as NVIDIA TensorRT and NVIDIA TensorRT-LLM, which deliver low-latency, high-throughput performance. The microservices also offer easy and fast API integration with standard frontends like the OpenAI API or LangChain for Python environments.
ImgSrc: blogs.nvidia.co
References :
Research efforts at Nvidia are not limited to theoretical advancements. The company is also developing tools that streamline the integration of AI models into real-world applications. One notable example is the work being done with NVIDIA NIM microservices, which are being leveraged by researchers at the University College London (UCL) Deciding, Acting, and Reasoning with Knowledge (DARK) Lab to benchmark agentic LLM and VLM reasoning for gaming. These microservices simplify the deployment and scaling of AI models, enabling researchers to efficiently handle workloads of any size and customize models for specific needs.
Nvidia's NIM microservices are designed to redefine how researchers and developers deploy and scale AI models, offering a streamlined approach to harnessing the power of GPUs. These microservices simplify the process of running AI inference workloads by providing pre-optimized engines such as NVIDIA TensorRT and NVIDIA TensorRT-LLM, which deliver low-latency, high-throughput performance. The microservices also offer easy and fast API integration with standard frontends like the OpenAI API or LangChain for Python environments.
ImgSrc: blogs.nvidia.co
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References :
- developer.nvidia.com: Researchers from the University College London (UCL) Deciding, Acting, and Reasoning with Knowledge (DARK) Lab leverage NVIDIA NIM microservices in their new research on benchmarking agentic LLM and VLM reasoning for gaming.
- BigDATAwire: Nvidia is actively involved in research related to multimodal generative AI, including efforts to improve the reasoning capabilities of LLM and VLM models for use in gaming.
Classification:
- HashTags: #NvidiaAI #GAssist #AIResearch
- Company: Nvidia
- Product: G-Assist
- Feature: G-Assist plugin builder
- Type: AI
- Severity: Informative