Modi Tamam developed multi-node vLLM inference support for the vllm-project/aibrix repository, enabling scalable distributed inference using RayClusterFleet and the Qwen2.5-Coder-7B-Instruct model. He designed and implemented head and worker node configurations, integrated an aibrix runtime sidecar, and defined service definitions and HTTP routing to support distributed workloads. His work involved orchestrating resources, container commands, and networking within Kubernetes, leveraging YAML for configuration. By closing integration gaps and providing a complete RayClusterFleet example, Modi improved deployment readiness and reliability, allowing faster onboarding and efficient scaling of large language model inference in distributed machine learning operations environments.