This article highlights OpenMetal’s perspective on AI infrastructure, as shared by Todd Robinson at OpenInfra Days 2025. It explores how OpenInfra, particularly OpenStack, enables scalable, cost-efficient AI workloads while avoiding hyperscaler lock-in.
Category: AI and ML
At OpenMetal, you can deploy AI models on your own infrastructure, balancing CPU vs. GPU inference for cost and performance, and maintaining full control over data privacy.
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The bare metal cloud market is poised for significant growth in the coming years, fueled by the rapid advancements in artificial intelligence (AI) and machine learning (ML), as well as the increasing demand for high-performance computing (HPC).
High performance computing refers to the use of powerful computers and parallel processing techniques to solve complex computational problems. HPC is typically used for tasks that can include: running larging-scale simulations, financial models, big data analytics and AI which require considerable processing power, memory and storage. Private OpenStack clouds offer several key features such as scalability, flexibility, integration and cost-efficiency that make them suitable to for running HPC workloads.
ELIZA (created by Joseph Weizenbaum) was one of the first natural language processing programs designed to simulate conversation. But it wasn’t until nearly 50 years later that AI tools like Siri allowed broad adoption so that many users could use natural language to interact with their devices. And while Siri had its time, never before have we had this many significant consecutive releases (ChatGPT, Gemini, Alexa) that transformed our daily lives. AI now plays a pivotal role in our daily lives, answering our questions, articulating and documenting our ideas in written and even artistic forms, driving our vehicles, cooking our food, analyzing our patterns, and recommending ads to us, etc. So what do you need to know about AI? And where do you begin if you want to leverage the endless possibilities of AI for your organization?
Nvidia is adapting to both AI and improvements needed in data center GPUs for non-AI work. View a comparison of their GPUs here.
When it comes to raw performance metrics, GPUs often lead the pack. Their architecture is specifically tailored for the high-speed, parallel processing demands of AI tasks. However, this doesn’t always translate into a one-size-fits-all solution.
In this article, we’ll explore scenarios where CPUs might not only compete but potentially gain an upper hand, focusing on cost, efficiency, and application-specific performance.
Artificial Intelligence (AI) and Machine Learning (ML) have been prominent topics within the technology landscape for an extended period. However, the emergence of AI such as OpenAI’s GPT-3 and Google Bard has elevated the excitement surrounding these advancements. GPT-3 stands as a language model capable of generating remarkably human-like text, garnering significant attention as a transformative force in the AI realm. Yet, how do these AI and ML technologies integrate with the realm of cloud computing? Moreover, what role do open-source cloud platforms like OpenStack play in propelling the progress of such sophisticated technologies?
OpenStack is a powerful cloud computing platform that is backed by a vast community of developers who continuously update and improve the software. In this blog, we will discuss OpenStack projects and open source software that can be used to create a cloud environment that’s ideal for building, testing, and managing AI.