A production-focused guide to distributed vLLM deployment with Ray, Kubernetes orchestration, tensor and pipeline parallelism, VRAM planning, and operations practices.
Who This Is For
Platform engineers planning distributed LLM serving on heterogeneous GPU infrastructure.
Summary
This article focuses on production deployment of LLM inference services. It discusses how Kubernetes and Ray solve different layers of the scaling problem, how vLLM can be deployed in distributed settings, and how GPU memory constraints shape architecture decisions.
Key Takeaways
- Kubernetes manages service lifecycle, availability, scheduling, and networking.
- Ray and Ray Serve help scale model serving logic across machines.
- Tensor parallelism and pipeline parallelism have different hardware assumptions and tradeoffs.
- VRAM planning is central when deploying large models on mixed GPU nodes.
Implementation Focus
- Separate cluster-level orchestration from model-serving-level scheduling and scaling.
- Plan GPU memory first: model weights, KV cache, batch size, parallelism, and headroom all affect stability.
- Use Kubernetes for lifecycle and isolation while letting Ray or the serving stack coordinate distributed inference logic.
Practical Use Cases
- Designing a containerized LLM serving platform for internal use.
- Planning Ray plus vLLM deployments on heterogeneous GPU nodes.
- Explaining why distributed inference is constrained by memory topology, not only raw compute.
Why It Matters
The post shows a practical bridge between LLM serving theory and real deployment constraints such as heterogeneous GPUs and containerized operations.