記事の概要
LLM-D is introduced as a new open-source framework designed for Kubernetes-native distributed inference of large language models.
- It aims to simplify the deployment and scaling of LLMs, managing resources like GPUs and CPU-only nodes efficiently.
- LLM-D allows for flexible model slicing and pipelining across multiple nodes and GPUs, optimizing resource utilization.
- The framework supports various inference runtimes, including vLLM and TensorRT-LLM, ensuring compatibility with different model architectures.
- It features a Kubernetes Operator for streamlined lifecycle management of LLM inference services within a Kubernetes cluster.