Optimizing AI inference is crucial for any enterprise looking to scale their AI strategies. NVIDIA NIM (NVIDIA Inference Microservices) on GMI Cloud is designed to do just that — by providing a seamless, scalable solution for deploying and managing AI models. NIM leverages optimized inference engines, domain-specific CUDA libraries, and pre-built containers to reduce latency and improve throughput. This ensures your AI models run faster and more efficiently, delivering superior performance. Join us as we showcase a demo and dive into the benefits of NVIDIA NIM on GMI Cloud.
Optimizing AI Inference with NVIDIA NIM on GMI Cloud
NVIDIA NIM is a set of optimized cloud-native microservices designed to streamline the deployment of generative AI models. GMI Cloud’s full-stack platform provides an ideal environment for leveraging NIM due to its robust infrastructure, access to top-tier GPUs, and integrated software stack.
Demo Video
Step-by-Step Guide
Log in to the GMI Cloud Platform
- Create an account or log in using a previously created account.
Navigate to the Containers Page
- Use the navigation bar on the left side of the page.
- Click the ‘Containers’ tab.
Launch a New Container
- Click the ‘Launch a Container’ button located in the upper right-hand corner.
- Select the NVIDIA NIM container template from the dropdown menu.
Configure Your Container
- Choose the Llama 38B NIM container template from the NVIDIA NGC catalog.
- Select hardware resources such as the NVIDIA H100, memory, and storage capacity.
- Enter the necessary details for storage, authentication, and container name.
Deploy the Container
- Click ‘Launch Container’ at the bottom of the configuration page.
- Return to the ‘Containers’ page to view the status of your newly launched container.
- Connect to your container via the Jupyter Notebook icon.
Run Inference and Optimize
- Within the Jupyter Notebook workspace, add functions for inference tasks.
- Utilize the pre-built NIM microservices to run optimized inference on your model.
- Test and validate performance
The Benefits of Optimizing AI Inference with NVIDIA NIM on GMI Cloud
Deploy Anywhere
- NIM’s portability allows deployment across various infrastructures, including local workstations, cloud environments, and on-premises data centers, ensuring flexibility and control.
Industry-Standard APIs
- Developers can access models via APIs adhering to industry standards, facilitating seamless integration and swift updates within enterprise applications.
Domain-Specific Models
- NIM includes domain-specific CUDA libraries and code tailored for language, speech, video processing, healthcare, and more, ensuring high accuracy and relevance for specific use cases.
Optimized Inference Engines
- Leveraging optimized engines for each model and hardware setup, NIM provides superior latency and throughput, reducing operational costs and enhancing user experience.
Enterprise-Grade AI Support
- Part of NVIDIA AI Enterprise, NIM offers a solid foundation with rigorous validation, enterprise support, and regular security updates, ensuring reliable and scalable AI applications.
Why Choose GMI Cloud for AI Inference Optimization
Accessibility
- GMI Cloud offers broad access to the latest NVIDIA GPUs, including the H100 and H200 models, through its strategic partnerships and Asia-based data centers.
Ease of Use
- The platform simplifies AI deployment with a rich software stack designed for orchestration, virtualization, and containerization, compatible with NVIDIA tools like TensorRT.
Performance
- GMI Cloud’s infrastructure is optimized for high-performance computing, essential for training, inferencing, and fine-tuning AI models, ensuring efficient and cost-effective operations.
Conclusion
Optimizing AI inference with NVIDIA NIM on GMI Cloud provides enterprises with a streamlined, efficient, and scalable solution for deploying AI models. By leveraging GMI Cloud’s robust infrastructure and NVIDIA’s advanced microservices, businesses can accelerate their AI deployments and achieve superior performance.
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