Operations | Monitoring | ITSM | DevOps | Cloud

Sovereign AI ensures control over AI/ML data, models, and infrastructure, which is now essential for enterprises, regulated industries, and national interests. JFrog and NVIDIA have collaborated to deliver a secure, scalable solution for sovereign AI. NVIDIA provides the accelerated computing and AI software while JFrog ensures trusted DevSecOps and MLOps practices across the entire AI lifecycle, from model development and security scanning to deployment at the edge and in air-gapped environments.

Across the Asia Pacific region significant investment is going into new subsea cable infrastructure that will help sustain the long-term demand for AI. We’ve written a lot on this blog about the impact of AI on networks and how AI workloads require low latency, high-capacity data transfer. This in turn puts more pressure on existing network infrastructure and in particular subsea cable systems - which provide the global backbone for cloud platforms and data centres.

To ease the path of enterprise AI adoption and accelerate the conversion of AI insights into business value, NVIDIA recently published the NVIDIA Enterprise AI Factory validated design, an ecosystem of solutions that integrates seamlessly with enterprise systems, data sources, and security infrastructure. The NVIDIA templates for hardware and software design are tailored for modern AI projects, including Physical AI & HPC with a focus on agentic AI workloads.

As organizations bring more AI and LLM workloads into production, the underlying GPU infrastructure that supports these workloads becomes even more critical in ensuring these workloads remain fast, reliable, and scalable. Inefficient GPU resource usage, for instance, can lead to longer runtimes and reduced throughput, negatively impacting overall model performance. Additionally, idle and underutilized GPUs can quickly drive up costs and lead to needless spending.

As development teams adopt AI-powered tools and build services that make use of AI agents, they want to extend their AI capabilities to incorporate familiar tools and observability data. However, AI agents struggle with regular API endpoints and frequently fail when parsing complex nested JSON hierarchies or incorrectly handling errors. As a result, these agents often fail to retrieve relevant results.