Generative AI has the potential to transform every industry. Human workers are already using large language models (LLMs) to explain, reason about, and solve difficult cognitive tasks. Retrieval-augmented generation (RAG) connects LLMs to data, expanding the usefulness of LLMs by giving them access to up-to-date and accurate information. Many enterprises have already started to explore how…
]]>This is the second post in a two part series. The first post described how to integrate the NVIDIA GPU and Network Operators using preinstalled drivers. This post describes the following tasks: The preinstalled driver integration method is suitable for edge deployments requiring signed drivers for secure and measured boot. Use the driver container method when the edge…
]]>NVIDIA Operators simplify GPU and SmartNIC management on Kubernetes. This post shows how to integrate NVIDIA Operators into new edge AI platforms using preinstalled drivers. This is the first post in a two-part series. The next post describes how to integrate NVIDIA Operators using custom driver containers. Today every industry uses edge AI. Servers deployed in airplanes, stores…
]]>The modern data center is becoming increasingly difficult to manage. There are billions of possible connection paths between applications and petabytes of log data. Static rules are insufficient to enforce security policies for dynamic microservices, and the sheer magnitude of log data is impossible for any human to analyze. AI provides the only path to the secure and self-managed data…
]]>Inference has emerged as THE killer app for edge computing due to its flexibility. Today, inference at the edge (also called edge AI) solves problems across every industry: preventing theft, detecting illness, and reducing herbicide use in farms. But for many, the complexity associated with managing distributed edge servers can erode the business value. An edge AI data center does not have 10…
]]>Edge computing takes place close to the data source to reduce network stress and improve latency. GPUs are an ideal compute engine for edge computing because they are programmable and deliver phenomenal performance per dollar. However, the complexity associated with managing a fleet of edge devices can erode the GPU’s favorable economics. In 2019, NVIDIA introduced the GPU Operator to…
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