LLMs: What DevOps Engineers Actually Need to Know

Everyone is talking about LLMs. Few understand how they work. Even fewer know how to run them in production.

LLM ≠ Magic

LLM stands for Large Language Model — a deep neural network trained to predict the next word in a sentence. It’s not AI magic. It's a statistical engine backed by massive infrastructure.

If you’re a DevOps engineer, you don’t need to know the math.
But you do need to know the stack.

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🧱 The LLM Infrastructure Stack (Kubernetes-Friendly)

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Key Concepts (Buzzwords Decoded)

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Kubernetes-Specific Tips

• Use GPU node pools with NVIDIA A100/H100 or similar

• Enable MIG partitions to run smaller jobs in parallel

• Mount model files using CSI or NFS from MinIO/S3

• Deploy inference server as a Deployment or Job (depending on latency vs batch)

• Expose with Ingress + Service + API Gateway

• Use ResourceQuotas to protect GPU resources

• Monitor with DCGM Exporter + Prometheus + Grafana Dashboards

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Hype vs. Reality

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🧭 Should You Care?

Yes — if any of this is true:

• Your org wants to self-host LLMs

• You need GPU scheduling in Kubernetes

• You’re building or scaling model inference APIs

• You want to optimize token throughput, latency, and cost

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🗺️ Coming Next Week

"Service Mesh: What It Solves, When It Hurts"
Spoiler: You don’t need one until you’re running dozens of services at scale.

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