Inference-as-a-Service (IaaS) for AI industrial solutions (Industry 4.0) – NVIDIA supports new inference service from Hugging Face

🤖🌐 Nvidia delivers its new inference-as-a-service offering through the Hugging Face model repository platform while introducing new microservices for industrial generative AI use cases. These advances promise to revolutionize both industrial users and robot manufacturers alike.

At SIGGRAPH 2024, one of the world's most important conferences for computer graphics and interactive technologies, Nvidia presented its latest developments in the field of artificial intelligence. The AI ​​computing giant announced a new inference-as-a-service offering from Hugging Face, highlighting the integration of new microservices for industrial generative AI use cases.

🤖🛠️ Inference-as-a-Service for AI industry solutions

Inference-as-a-Service (IaaS) is a model that makes it possible to bring machine learning (ML), and in particular deep learning, to applications without requiring end users to have extensive knowledge of the underlying models or the Must have infrastructure. This involves the provision of inference services via a cloud platform.

Here are some key points about Inference-as-a-Service:

1. 🔍 Provision of models

IaaS providers provide pre-trained models aimed at various applications such as image recognition, speech recognition, word processing, and more.

2. 📈 Scalability

Because IaaS is cloud-based, it can be flexibly scaled to meet users' needs without them having to manage their own hardware or software.

3. 🔧 Easy integration

IaaS services are often available as APIs that can be easily integrated into existing applications. This makes it easier for developers to use ML models without requiring deep expertise in data science and machine learning.

4. 💰 Cost optimization

Users typically only pay for actual use of the services. This can be more cost-effective than running your own ML infrastructure, especially for small or medium-sized businesses.

5. 🔄 Maintenance and Updates

The provider takes care of maintaining, updating and optimizing the models and infrastructure so that users can focus on their core applications.

🚀 Inference-as-a-Service: Accessible, Affordable and Scalable

Overall, Inference-as-a-Service provides an accessible, cost-effective and scalable way to harness the power of machine learning and integrate it into various applications.

🤝 NVIDIA and Hugging Face collaboration

Nvidia's goal is to further simplify and make the development of AI applications more efficient. This is done by supporting a new Inference-as-a-Service service provided through the Hugging Face model repository. It was announced at the conference that this service will run on the Nvidia DGX cloud service and leverage a number of Nvidia's inference microservices. This is intended to help developers quickly integrate popular large language models, such as Meta's Llama 3 family and Mistral's AI models, into their applications.

The official name for these microservices is Nvidia NIM. These services consist of various AI models that are offered in optimized containers and can be seamlessly integrated into various applications by developers. Nvidia introduced these microservices in early June, and since then they have supported more than 40 models provided by Nvidia and other developers.

📦 Offering the Nvidia AI Enterprise software suite

NIM is accessed through the Nvidia AI Enterprise software suite, which costs $4,500 per GPU per year. However, this service is provided free of charge to developers who are members of the Nvidia Developer Program.

Hugging Face's new inference service offers developers the ability to "quickly prototype with open source AI models hosted on the Hugging Face Hub and bring them into production," the Santa Clara-based company explained. California.

This service complements Hugging Face's existing Train on DGX Cloud Service, which was introduced a year ago at SIGGRAPH 2023 together with Nvidia.

🚀 New NIM microservices for industrial GenAI use cases

In addition to the inference service, Nvidia also announced the introduction of new NIM microservices at SIGGRAPH 2024. These are intended to help developers bring generative AI capabilities to sectors such as manufacturing and robotics. These new services include the “world’s first generative AI models for OpenUSD development.”

OpenUSD is an open 3D framework developed by Pixar that Nvidia uses to connect its Omniverse platform with other 3D applications. The new NIM microservices enable developers to, among other things, search libraries of OpenUSD, 3D and image data using text or image input, generate realistic materials for 3D objects, compose OpenUSD-based scenes using text input, and resolve physics -Improve simulations with AI-based upscaling.

🔗 Integration of AI and 3D frameworks

Nvidia also released a connector that links data between the Unified Robotics Description Format and OpenUSD. This technology is intended to help developers transfer and synthesize robotics data across design, simulation and reinforcement learning applications. Additionally, Nvidia is offering developers the ability to develop their own OpenUSD data connections using the new OpenUSD Exchange Software Development Kit.

“Until recently, digital worlds were primarily used by creative industries; Now, with the improvements and accessibility of NVIDIA NIM Microservices for OpenUSD, industries of all types can build physically based virtual worlds and digital twins to drive innovation and prepare for the next wave of AI: robotics,” said Rev Lebaredian, Vice President of Omniverse and Simulation technology at Nvidia.

🤖 Revolutionary applications in industry and robotics

The integration of generative AI into industrial processes opens up completely new possibilities for increasing efficiency and automation. In the manufacturing industry, for example, AI models can be used to optimize production processes by predicting machine maintenance and breakdowns or monitoring the quality of products in real time. By using AI and the new NIM microservices, robot manufacturers can make their robotic systems smarter and more flexible, resulting in better adaptation to different tasks and environments.

With the ability to generate realistic materials for 3D objects and compose scenes based on text input, design and simulation processes can also be significantly accelerated and improved. These technologies enable companies to shorten their development cycles and respond more quickly to market requirements.

🌟 Significance for the future of AI technology

Nvidia's foray into generative AI and collaboration with Hugging Face marks a significant step in the advancement of AI technology. The availability of inference microservices and advanced frameworks like OpenUSD ensures developers have the tools necessary to build the next generation of AI applications.

The relevance of these developments is particularly clear in industries that are heavily dependent on precise and efficient processes. In the automotive industry, for example, AI models could be used to optimize the production of vehicles by planning the use of materials and resources more efficiently. In logistics, autonomous systems controlled by generative AI could improve the supply chain and make predictions about delivery times and routes.

💡 Innovations from Nvidia and Hugging Face

The innovations from Nvidia and Hugging Face presented at SIGGRAPH 2024 give an idea of ​​the enormous potential that lies in the combination of AI and industrial applications. Providing inference microservices and leveraging the OpenUSD framework enables developers to solve complex tasks in manufacturing, robotics, and beyond. These advances are not only an important step for Nvidia and Hugging Face, but also for the entire artificial intelligence industry and its users. The future of Industry 4.0 will be significantly shaped by these technologies, which enable intelligent, automated and more efficient processes.

📣 Similar topics

• 📣 Inference service: Nvidia and Hugging Face are revolutionizing AI
• 🚀 New microservices for industrial generative AI
• 🤝 Collaboration: Nvidia and Hugging Face join forces
• 🛠️ NIM Microservices: New tools for AI developers
• 💡 AI integration with 3D frameworks: OpenUSD and more
• 🏭 Application in industry: increasing efficiency through generative AI
• 🧩 Nvidia AI Enterprise software suite: Pricing and features
• 🤖 Advances in robotics through Nvidia NIM microservices
• 📊 Importance for industries: Focus on manufacturing and robotics
• 🌐 Pioneering AI technologies from Nvidia and Hugging Face

#️⃣ Hashtags: #Nvidia #HuggingFace #GenerativeKI #Industrie40 #Robotics

A fascinating recent example is a video released by NVIDIA demonstrating how to control a robot using the Apple Vision Pro. In this scenario, a human is in a kitchen and controls a robot by adopting the robot's perspective through the Vision Pro glasses. The hand movements detected by the glasses are transmitted to the robot, allowing humans to control the robot remotely. This enables applications such as the preparation of toast with honey, controlled by humans.

This technology has far-reaching implications, especially in areas where it can be dangerous for people, such as collapsing buildings or other dangerous environments. It's easy to imagine how this technology could be used in rescue missions or to defuse bombs.

More about it here:

• NVIDIA and the Future of Humanoid Robotics: A Look at Developments in Augmented Reality and AI

The future of industrial AI looks promising. The integration of AI into industrial processes will continue to increase and offer even more opportunities for innovation. Key trends driving this development include the networking of machines (Internet of Things), the increasing availability of big data, and advances in algorithms and hardware.

An interesting development direction is the autonomous factory. Here, robots and other intelligent systems interact with each other, exchange data in real time and optimize the production process without human intervention. This could lead to a revolution in the manufacturing industry, similar to the introduction of assembly line manufacturing in the early 20th century.

More about it here:

• In the modern industrial landscape – The importance of robotics in industry

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