Running an LLM in a notebook is easy. Running it in production with predictable latency, sane GPU costs, and safe deployments is where the real work starts.
For most teams serving open-source models, vLLM + Kubernetes is the gold standard. vLLM provides efficient inference through paged attention and continuous batching, while Kubernetes handles multi-node inference, GPU autoscaling, and operational consistency.
The Production vLLM Playbook
1. Accurate Capacity Planning
VRAM is your primary constraint. You must right-size your nodes based on model weights and KV cache requirements to avoid OOMs and high latency. This is especially true for Mixture-of-Experts (MoE) models, which have unique memory and serving requirements.
2. Intelligent Routing and Gateways
Don't expose your model servers directly. Use an LLM Gateway to manage rate limits, token economics, and PII filtering.
3. Integrated Observability
A production vLLM deployment is incomplete without deep observability. Track TTFT, tokens per second, and GPU duty cycle using Prometheus and Grafana.
4. Zero-Downtime Updates
Ensure that model updates don't interrupt service by using blue-green or rolling deployments.
Final Takeaway
vLLM on Kubernetes is the most powerful combination for serving open-source LLMs today. By focusing on VRAM right-sizing, gateway-level controls, and deep observability, you can build a serving stack that is both high-performing and operationally stable.
Need help deploying or optimizing vLLM on Kubernetes? We help teams design GPU-efficient serving stacks, implement smart routing, and build production-grade observability for LLMs. Book a free infrastructure audit and we’ll review your serving architecture.
