As enterprises adopt generative AI, many face challenges with cloud-based models, including costs and privacy issues. Red Hat addresses these concerns with llm-d, an open-source framework for running language models in production using Kubernetes. According to Tushar Katarki, Head of Product for GenAI, customers desire control similar to OpenAI services but with the flexibility of on-prem or private cloud deployments.
llm-d enhances model inferencing efficiency while ensuring cost management and compliance. It enables platform engineers to create “model-as-a-service” layers, optimizing performance for varying application needs. Supporting diverse hardware, including NVIDIA and AMD accelerators, llm-d caters to a multi-model future—integrating large, small, and predictive models for complex tasks.
Ultimately, Red Hat’s strategy emphasizes treating AI as managed infrastructure rather than mere research, focusing on performance, governance, and security to meet enterprise demands. With llm-d, businesses can industrialize AI deployment while maintaining operational reliability and innovative flexibility.
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