Kubernetes LLM Serving: Chunked Prefill and PagedAttention in Production

Deploying Large Language Models at scale on Kubernetes is not merely about spinning up a Pod with a declarative GPU resource request.

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1. Concurrency Breakdown in Standard Deployments

Under concurrent enterprise load, the compute-bound prompt prefill phase chokes ongoing memory-bound token decode iterations, destroying Time-To-First-Token (TTFT) predictability and causing severe tail latencies.

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2. Decoupled Execution with vLLM & KServe

Pairing Chunked Prefill with PagedAttention inside modern inference runtimes fundamentally stabilizes serving performance:

• Near-Zero VRAM Fragmentation: KV-Cache blocks are dynamically allocated into non-contiguous memory pages on demand.
• Stable P99 Latency: Interleaving sliced prompt chunks across engine iterations prevents decode starvation.
• Deterministic GPU Memory Utilization: Eliminates Out-Of-Memory (OOM) failures under heavy request batching.

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3. Production Engineering in AS300

These core architectural primitives are built hands-on in the AS300 (AI Platform Engineer) curriculum at Aperta Scientia:

• Fine-tuning KServe ServingRuntimes on Red Hat OpenShift.
• Hardware acceleration profiling across NVIDIA GPU clusters.
• End-to-end Prometheus SLO metrics for inference monitoring.

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4. Technical Sources & References

• Kubernetes GPU Scheduling & Topology: Kubernetes Official Docs
• vLLM KV Cache & Memory Management: vLLM Core Architecture
• KServe vLLM Runtime on OpenShift: KServe GitHub