Red Hat OpenShift AI now enables on-premise LLM inference for enterprise use cases, ensuring compliance and resource control.
Red Hat has published a technical guide outlining how to deploy large language model (LLM) inference services on OpenShift AI, a platform designed for enterprise environments. This approach allows organizations to containerize, scale, and integrate LLM workloads directly into their existing infrastructure, providing better control over data privacy, compliance with internal policies, and the ability to optimize resource utilization. By leveraging OpenShift AI, enterprises can deploy models like the Ansible Lightspeed intelligent assistant on-premise, avoiding the risks associated with cloud-based AI services and ensuring sensitive data remains within organizational boundaries[5].\n\nThe deployment process is supported by GuideLLM, a Python-based tool for benchmarking and evaluating LLM inference performance under various workloads. GuideLLM enables precise simulation of real-world usage, helping IT teams assess model latency, throughput, and costs across different hardware configurations. As enterprises increasingly adopt generative AI, solutions like OpenShift AI are critical for bridging the gap between powerful LLMs and the stringent requirements of regulated industries, enabling secure, scalable, and compliant AI deployments[5].
Source: https://developers.redhat.com/articles/2025/11/03/deploy-llm-inference-service-openshift-ai Red Hat Developer