ConfidentialMind reposted this
🔒 🧠 ConfidentialMind's first KubeCon North America was a great success. Many discussions around LLM deployment and security. Some recurring themes: 1. Big enterprises doing partial public cloud exits especially in AI inference for data governance and cost reasons 2. Universities investing heavily into on-prem LLM inference 3. Companies looking to license their scalable LLM backend versus doing it themselves Gladly, we hit all these themes with our platform that allows your to deploy, connect, and secure LLMs in enterprise environments. It was a privilege to learn about the businesses and projects of so many organizations that came to chat with us during the convention. Severi Tikkala, Esko Vähämäki #KubeCon #Kubernetes #GenerativeAI
Being on the floor with Markku Räsänen, I have to agree that I saw more demand for on-prem+self-service genAI than we ever could have expected!
Co-Founder of Altrosyn and DIrector at CDTECH | Inventor | Manufacturer
1wThe trend of enterprises selectively exiting public clouds for AI inference, driven by data governance and cost concerns, is fascinating. This suggests a growing need for robust on-premises solutions that can handle the computational demands of large language models while ensuring strict data security. The rise of universities investing in on-premises LLM inference further highlights this shift towards localized AI infrastructure. It will be interesting to see how open-source projects like Kubernetes evolve to meet these specific requirements. Do you envision a future where specialized, secure Kubernetes distributions emerge for enterprise-grade LLM deployments?