# Distributed Inference: Disaggregated Prefill & Decode with vLLM and KubeRay

> Scaling LLM inference in production: isolating compute-bound prefill from memory-bound decode stages using vLLM and KubeRay on OpenShift AI.

- **Date de publication** : 2026-08-31T09:00:00.000Z

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Scaling LLM inference in production requires moving beyond monolithic worker node architectures. When concurrent requests hit an inference cluster, unoptimized batching creates severe contention between prompt ingestion and token generation.

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### 1. The Core Infrastructure Bottleneck
Standard serving pipelines execute two fundamentally different computing profiles on the same hardware:
* **Prefill Phase (Compute-Bound):** Highly parallelized computation to ingest context and build the initial KV-Cache.
* **Decode Phase (Memory-Bandwidth-Bound):** Token-by-token generation strictly limited by VRAM memory bandwidth.

Under heavy traffic, resource-heavy prefill operations stall ongoing decode iterations, resulting in severe Time-To-First-Token (TTFT) latency spikes and unpredictable tail latencies.

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### 2. Architectural Solution: PD-Disaggregation
Disaggregated Prefill & Decode separates these stages across dedicated GPU worker pools:
* **Dedicated Prefill Workers:** Tuned for maximum tensor computation throughput.
* **Dedicated Decode Workers:** Optimized for memory bandwidth and continuous batching.
* **High-Speed KV Transfer:** Fast tensor streaming across nodes over high-bandwidth interconnects (**NCCL / RoCE / InfiniBand**).

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### 3. Enterprise Operations & Aperta Curriculum
On Red Hat OpenShift AI leveraging **vLLM** and **KubeRay**:
* P99 latency stabilizes deterministically under high concurrent load.
* GPU compute utilization is maximized across multi-tenant clusters.
* **AS300 (AI Platform Engineer) Track:** Deep-dive implementation in our *Distributed Serving & MLOps* module, configuring multi-node Ray clusters and hardware-accelerated KServe runtimes on production bare-metal infrastructure.
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### 4. Technical Sources & References
* **vLLM Distributed Serving & PD-Disaggregation:** [vLLM Documentation](https://docs.vllm.ai) & [vLLM Architecture Blog](https://blog.vllm.ai)
* **KubeRay on OpenShift:** [Ray Project Official Docs](https://docs.ray.io/en/latest/cluster/kubernetes/index.html)
* **Red Hat OpenShift AI Model Serving:** [Red Hat OpenShift AI Documentation](https://docs.redhat.com/en/documentation/red_hat_openshift_ai/)

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*Source : https://aperta.io/radar/2026-08-31-vllm-kuberay-disaggregated-prefill-decode/ — Aperta Scientia Tech Radar*