Uploaded July 2026 | Updated September 2026, 3 weeks ago
Don't miss out! Join us at our next KubeCon + CloudNativeCon events in Yokohama, Japan (29-30 July, 2026), and Shanghai, China (8-9 September, 2026) Salt Lake City, United States (Nov 9–12, 2026). Connect with our current graduated, incubating, and sandbox projects as the community gathers to further the education and advancement of cloud native computing. Learn more at kubecon.io
Beyond VLLM: Distributed LLM Inferencing With Llm-d on Kubernetes - Ravindra Patil, Red Hat
As (LLMs) continue to grow in size and demand, single-node inferencing quickly becomes a bottleneck for performance, scalability, and cost. While vLLM has become popular for efficient LLM serving on a single node, it does not fully address the challenges of distributed inferencing across multiple GPUs and nodes in Kubernetes environments.
This talk introduces llm-d, a emerging cloud-native project designed to enable distributed LLM inferencing on Kubernetes. We will cover why vLLM gained popularity and the limitations when scaling beyond a single node. We will explore how llm-d goes a step further by enabling multi-node, multi-GPU inferencing with cloud-native primitives.
Attendees will learn how llm-d fits into modern Kubernetes platforms, how it improves scalability and resource utilization. The session focuses on practical architecture, design trade-offs, and real-world use cases rather than theory with a demo on how llm-d distributes load.
Don't miss out! Join us at our next KubeCon + CloudNativeCon events in Yokohama, Japan (29-30 July, 2026), and Shanghai, China (8-9 September, 2026) Salt Lake City, United States (Nov 9–12, 2026). Connect with our current graduated, incubating, and sandbox projects as the community gathers to further the education and advancement of cloud native computing. Learn more at kubecon.io
Beyond VLLM: Distributed LLM Inferencing With Llm-d on Kubernetes - Ravindra Patil, Red Hat
As (LLMs) continue to grow in size and demand, single-node inferencing quickly becomes a bottleneck for performance, scalability, and cost. While vLLM has become popular for efficient LLM serving on a single node, it does not fully address the challenges of distributed inferencing across multiple GPUs and nodes in Kubernetes environments.
This talk introduces llm-d, a emerging cloud-native project designed to enable distributed LLM inferencing on Kubernetes. We will cover why vLLM gained popularity and the limitations when scaling beyond a single node. We will explore how llm-d goes a step further by enabling multi-node, multi-GPU inferencing with cloud-native primitives.
Attendees will learn how llm-d fits into modern Kubernetes platforms, how it improves scalability and resource utilization. The session focuses on practical architecture, design trade-offs, and real-world use cases rather than theory with a demo on how llm-d distributes load.
