How to Run Llama 3 on Kubernetes Without Crying

2026-05-22 · Source: LLM on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Intermediate, medium

Summary

Running Llama 3 on Kubernetes for large language model inference presents unique challenges, as Kubernetes is optimized for stateless microservices, not GPU-intensive, stateful LLM workloads. Issues include "Pending" pods due to GPU scarcity, prolonged cold starts, memory explosions, inefficient autoscaling, and low GPU utilization leading to high cloud costs. This guide outlines a practical approach, recommending starting with Llama 3 8B and a specific stack comprising Kubernetes, NVIDIA GPU Operator, vLLM for efficient batching, KEDA for queue-based autoscaling, and Prometheus/Grafana for observability. It addresses common problems like brutal cold starts by adjusting readiness probes, GPU fragmentation via node affinity, and OOM crashes through quantization or reduced context. The article also emphasizes robust traffic management and cost optimization strategies like aggressive quantization and maximizing batching.

Key takeaway

For MLOps Engineers deploying Llama 3 on Kubernetes, recognize that LLM inference is not a traditional microservice. You must adopt specialized tooling like vLLM for batching, KEDA for queue-based autoscaling, and the NVIDIA GPU Operator for efficient resource management. Prioritize comprehensive observability beyond CPU metrics and implement aggressive quantization to optimize costs. Ignoring these distinctions will lead to unstable scaling, poor performance, and significantly higher cloud bills.

Key insights

Running LLM inference on Kubernetes demands specialized tooling and a distinct approach from traditional microservices to avoid performance and cost issues.

Principles

• LLM inference differs fundamentally from stateless microservices.
• Maximize GPU utilization for cost-effective inference.
• Queue-based metrics drive effective LLM autoscaling.

Method

Prepare Kubernetes with GPU Operator, deploy vLLM for Llama 3, then address cold starts, GPU fragmentation, and OOM errors. Implement KEDA for queue-based autoscaling, add comprehensive observability, and manage traffic with an API Gateway.

In practice

• Start Llama 3 deployments with the 8B model.
• Install NVIDIA GPU Operator for seamless GPU setup.
• Utilize vLLM for efficient continuous batching.

Topics

• Kubernetes
• Llama 3
• LLM Inference
• GPU Management
• vLLM
• KEDA
• Cost Optimization

Best for: MLOps Engineer, AI Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by LLM on Medium.