Distributed Inference: Disaggregated Prefill & Decode with vLLM and KubeRay

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.

---

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.

---

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).

---

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.

---

4. Technical Sources & References

• vLLM Distributed Serving & PD-Disaggregation: vLLM Documentation & vLLM Architecture Blog
• KubeRay on OpenShift: Ray Project Official Docs
• Red Hat OpenShift AI Model Serving: Red Hat OpenShift AI Documentation