Che-Wen Chang’s Post

The H100 package for mainstream servers includes a five-year subscription with enterprise support for the NVIDIA AI Enterprise software suite, simplifying the adoption of high-performance AI and giving enterprises access to the AI frameworks and tools they need to create H100-accelerated AI workflows such as AI Contact Us - +91 9846 3344 08 #supermicro #sever #serverengineer #h100server #tech #technology #it #informationtechnology #storage

Open-source tools offer greater flexibility and security for edge AI deployments. When combined with an accelerated computing stack, these solutions enable professionals to deliver projects faster, reduce operational costs, and achieve more predictable results. Canonical, Lenovo, and NVIDIA have teamed up to deliver a validated reference architecture for edge AI, that empowers developers and researchers to deliver projects faster, reduce costs, and ensure predictable outcomes. Read our blog to learn more and get access to our step-by-step guide for setting up the hardware and software necessary to execute ML workloads: https://lnkd.in/dDuda27U

NeMo Retriever is a collection of microservices enabling semantic search of enterprise data. Yes, you can do it in many other ways, but what is the difference when using #nvidia software? Check this out

Loved this interview between Harry Stebbings and Guy Podjarny on 20VC. Harry: "Larry Ellison said that to enter the frontier model race, you need $100B. Do you agree or do you think we're seeing the democratization of cost to end that race? ... Did you contemplate building your own model?" Guy: "... I definitely don't want to compete with all of those (foundational models). But I also don't even want to pick amidst them. I want to be able to use the best technology available to me at any time." What I respect about his point is that great developers choose to differentiate vs. competing on the playing field of other providers. When Meta built its Llama model, it didn’t just build another foundational model. It built a model that you have access to the weights to and can run yourself. That's differentiation. Similarly, there are so many companies building chips to try to copy Nvidia. Doing that might cost you $10B, or more, but what problem are you solving if you succeed? LPUs on the other hand solved an unsolved problem, we made inference fast and cheap in a way that GPUs can't. We aren't trying to be a better Nvidia, we built our own field. Groq made AI inference fast.

AI is hardware and software ...

📢 We are excited to introduce Synopsys Simpleware Software Release W-2024.12, which brings enhanced user control over automation and efficiency. Integrate your own #AI #segmentation models through #NVIDIA #MONAI Bundles or use popular open-source models like #TotalSegmentator. Boost efficiency and streamline your workflow by customizing the #Simpleware ribbon user interface. Learn more about additional new features, product updates and key benefits of the new release here 👉 https://lnkd.in/eCnnt5a7

Not just another release! You can now import your own #AI #segmentation models, pre-packaged NVIDIA #MONAI Bundles, or #TotalSegmentator into Synopsys Simpleware Software and run inference! This means you can leverage our #FDA and #MDR cleared software to: 👉 Inspect and clean up your segmentation (Lets be honest, as great as we all think our AI tool are, they can always use an efficient, easy to use tool for cleanup and analysis) 👉 Define landmarks/planes 👉 Perform measurements 👉 Export STL, 3MF, NURBS or FE Meshes (Ansys, Abaqus, Comsol, LS Dyna etc.) for 3D Printing, Design and Computational Simulation. We want to embrace and contribute to the work being done in the #OpenSource world of AI training for 3D image segmentation. This is why the Simpleware University Bundle is an ideal way for academics and researchers to build their AI workflows and get around the financial barriers that hold this field back.

While #MONAI contains outstanding #medical #AI methods, much of its impact comes from its integration in high-quality, user-friendly platforms that support clinical and research workflows. Here is another great example of the impact of MONAI being amplified by its integration into a powerful, user-friendly platform. #OpenSource licensing is an amazing impact multiplier!

Interesting move from NVIDIA to acquire rain:ai and continue to build an AI their ecosystem, offering a more robust platform for developers and enterprises focused on #AI-driven projects and allowing a more #frugal approach by sharing GPU capabilities.

Integrating Alfresco with the GenAI Stack unlocks a powerful suite of GenAI services, significantly enhancing document management capabilities. - Docker allows you to easily switch between different Large Language Models (LLMs) of different sizes. Additionally, if you have access to GPUs, you can deploy a container with a GPU-accelerated LLM for faster inference. - Docker containers encapsulate the GenAI service, its dependencies, and runtime environment, ensuring consistent behavior across different environments. This portability allows you to develop and test the AI model locally and then deploy it seamlessly to various platforms. - The stack provides support for GPU-accelerated computing, making it well suited for deploying GenAI models in production environments. Docker’s declarative approach to deployment allows you to define the desired state of the system and let Docker handle the deployment details, ensuring consistency and reliability. - Docker facilitates integration between GenAI services and other applications deployed as containers. You can deploy multiple containers within the same Docker environment and orchestrate communication between them using Docker networking.