Inferencing LLMs at Scale with Kubernetes and vLLM
✍️ Co-Authors:
1. Aman Mundra
Table of contents
1. Introduction
2. Why Traditional LLM Inference Doesn’t Scale
3. What is vLLM and Why Is It Important?
4. Fundamentals of Kubernetes for LLM Inference
5. vLLM Architecture: Core Innovations
6. Deployment Patterns: Running vLLM on Kubernetes
7. Performance Benchmarks and Real-World Results
8. Best Practices for Production Deployments
9. Conclusion
1. Introduction
Large Language Models (LLMs) are driving transformative applications — from chatbots and search to smart coding assistants and document automation tools. However, running LLMs efficiently in production presents formidable infrastructure and engineering challenges, especially when serving hundreds or thousands of concurrent requests with low latency and high reliability.
This article explains how to use Kubernetes and vLLM to reliably serve LLMs at production scale, relying on proven best practices, latest research, and real-world production insights.
2. Why Traditional LLM Inference Doesn’t Scale
Serving LLMs in production is not just about running a big neural network, it’s about maximizing throughput, minimizing serving cost, and reliably handling bursts of traffic and long-running interactions.Press enter or click to view image in full size
Classic bottlenecks include:
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High memory consumption: Standard inference engines can waste huge amounts of GPU memory, especially with long sequences, due to inefficient management of Key-Value (KV) caches inside the attention mechanism.
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Inflexible batch handling: Static batching wastes compute as some requests finish earlier than others.
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Resource fragmentation: Allocation of memory and GPU across multiple sessions and users is suboptimal.
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Autoscaling complexity: Scaling inference reliably across GPUs and nodes isn’t trivial, especially for distributed, multi-user workloads.
As a result, organizations often face high serving costs, underutilized hardware, slow response times, and failed inference jobs.
3. What is vLLM and Why Is It Important?
vLLM (Virtual Large Language Model) is an open-source library designed for high-throughput, memory-efficient LLM inferencing and serving in distributed systems.
Key objectives:
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Reduce GPU and memory waste (nearly zero wasted KV cache)
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Support massive concurrency and batch sizes
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Offer robust support for popular LLM models (Llama, Mistral, Falcon, and more)
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Integrate seamlessly with orchestration frameworks like Kubernetes and MLOps pipelines
vLLM achieves this through a combination of innovative algorithms, such as PagedAttention, efficient CUDA kernels, and best-in-class batching and quantization strategies.
4. Fundamentals of Kubernetes for LLM Inference
Kubernetes has become the de-facto standard for orchestrating and scaling ML workloads:
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GPU Scheduling: Kubernetes, with NVIDIA’s device plugin, enables dynamic and fair allocation of GPUs across pods.
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Autoscaling: Horizontal Pod Autoscalers and tools like KEDA and Karpenter scale LLM serving pods based on live inference demand.
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Networking: Enables load balancing and high availability for inference endpoints.
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Isolation & Security: Namespaces, role-based access control, and resource quotas keep workloads secure and isolated.
Deploying LLMs at scale means combining the operational strengths of Kubernetes with inference platforms tuned for AI workloads — which is where vLLM excels.
5. vLLM Architecture: Core Innovations
PagedAttention
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Inspired by OS virtual memory management, PagedAttention replaces the standard contiguous KV cache in LLMs with a paged/block layout.
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Stores key and value tensors in non-contiguous blocks, managed via a KV block table.
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Dramatically reduces memory fragmentation and allows for efficient cache sharing across sequences and layers.
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Enables long-sequence and multi-user inference with virtually no memory waste.
Memory Management and KV Cache
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Dynamic and efficient allocation avoids the “stranding” of GPU memory on idle or fragmented requests.
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vLLM’s approach allows for running much larger batch sizes and handling spikes in user requests without OOM errors.
Continuous Batching & Quantization
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Incoming requests are seamlessly “batched” in real-time to maximize GPU utilization, reducing latency and wasted computation.
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FP16 and quantization techniques further reduce memory footprint and speed-up throughput.
Optimized CUDA Kernels
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Hand-tuned for vLLM workloads, especially operations like fused reshape and block writes during attention calculation, making every millisecond (and megabyte) count.
6. Deployment Patterns: Running vLLM on Kubernetes
GPU Scheduling
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Prepare a Kubernetes cluster with GPU-enabled nodes and install the NVIDIA device plugin.
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Use node selectors or taints/tolerations to allocate jobs requesting GPU to the right pods.
Model Serving with vLLM
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vLLM offers an OpenAI-compatible API server, easily containerized.
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Start the server per model:
Python
-m vllm.entrypoints.openai.api_server \
— model meta-llama/Llama-3–70B-Instruct \
— dtype auto \
— api-key <token>
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Integrate with API gateways, ingress, or service meshes to expose secure endpoints for client applications.
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Use continuous deployment and Helm charts for reproducible infrastructure-as-code rollouts.
Autoscaling & High Availability
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Scale pods based on Prometheus/Grafana metrics (requests/sec, GPU usage).
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Use KEDA or custom GPU-based horizontal pod autoscalers for dynamic scaling.
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Ensure pod anti-affinity to avoid single points of failure
.
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Rolling updates and liveness/readiness probes provide zero-downtime upgrades.
7. Performance Benchmarks and Real-World Results
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Throughput: vLLM consistently delivers 2x–4x higher throughput vs. legacy serving engines (FasterTransformer, Orca), especially at large batch sizes and with long-sequence prompts.
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Latency: Time to First Token (TTFT) and overall average latency remain stable even at high concurrent loads, thanks to efficient KV cache management.
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Scalability: Multi-node deployments with distributed inference (e.g., using Ray in conjunction, or stack like llm-d) show excellent scaling on 40B/70B parameter models across 4+ nodes and 8+ GPUs.
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Hardware Efficiency: Smart memory management means you can serve bigger models — or more users-per GPU, reducing total hardware cost and energy footprint.
8. Best Practices for Production Deployments
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Always use GPU-enabled clusters with up-to-date NVIDIA drivers and CUDA libraries.
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Pin model and vLLM version in Dockerfiles for reproducibility.
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Run profiling on representative workloads — adjust batch sizes, sequence lengths, and quantization for your real traffic patterns.
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Monitor application health via Prometheus, visualize via Grafana.
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Secure endpoint access with API keys, service accounts, and network policies.
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Store logs and error traces centrally for debugging.
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Use Helm or GitOps tools (ArgoCD, Flux) for safe roll-outs.
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Scale nodes and pods based on real-time metrics (requests/sec, waiting jobs, GPU utilization).
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Where advanced customization is needed, consider KServe with custom Python predictors for vLLM.
9. Conclusion
Efficient LLM inferencing at scale is not just about raw horsepower — it’s about intelligent infrastructure. With Kubernetes for orchestration and vLLM for high-throughput model serving, organizations can run production LLM applications that are fast, scalable, and efficient. When combined with robust MLOps, teams achieve true agility, lower costs, and better real-world reliability.
vLLM’s impressive innovations in memory management, continuous batching, and flexible deployment make it a must-have for any engineering leader aiming to succeed with LLMs at scale.