CPU / GPU / TPU / NPU in Kubernetes & MLOps

 

1️⃣ CPU / GPU / TPU / NPU in Kubernetes & MLOps

🧠 CPU — Control Plane & Orchestration

In Kubernetes:

• Runs:

  • API servers

  • Controllers

  • CI/CD tools

  • FastAPI / backend services

• Handles:

  • Request routing

  • Feature engineering

  • Pre/post-processing

📌 In MLOps

• Data ingestion

• Model orchestration

• Pipeline coordination (Airflow, Kubeflow)

• Lightweight inference

👉 CPUs coordinate, not accelerate.

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🟢 GPU — Training & High-Throughput Inference

In Kubernetes:

• GPUs are exposed as extended resources

• Pods explicitly request them

• Managed via NVIDIA device plugin

📌 In MLOps

• Model training

• Batch inference

• LLM serving

• Embedding generation

👉 GPUs do heavy parallel math.

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🟠 TPU — Large-Scale AI Training

In Kubernetes:

• Mostly used in Google Cloud

• Integrated via specialised runtimes

📌 In MLOps

• Training massive neural networks

• Research-scale workloads

👉 TPUs are specialised accelerators, not general compute.

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🟣 NPU — Edge & Low-Latency AI

In Kubernetes:

• Rare in standard clusters

• Used in:

  • Edge Kubernetes

  • AI PCs

  • Embedded systems

📌 In MLOps

• On-device inference

• Privacy-preserving AI

• Real-time decisions

👉 NPUs run AI close to the user.

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2️⃣ 🎯 GPU Scheduling in Kubernetes (Interview Gold)

How Kubernetes schedules GPUs

• GPUs are not shared by default

• You request them explicitly:

resources:
  limits:
    nvidia.com/gpu: 1

What happens:

• Scheduler places the pod on a node with a free GPU

• GPU is exclusively assigned to that pod

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Advanced GPU Scheduling Concepts

• Node labels & taints
  → ensure only GPU workloads land on GPU nodes

• GPU pools
  → separate training vs inference clusters

• Autoscaling
  → scale GPU nodes based on demand

• Cost optimisation
  → avoid idle GPUs (very expensive!)

👉 Senior takeaway:

“GPUs must be carefully scheduled to balance performance, cost, and availability.”

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3️⃣ 🧪 Real Cloud Instance Examples (Practical)

AWS

• CPU: t3, m5

• GPU: g5, p4d

• Used for:

  • LLM inference

  • Model training

Azure

• CPU: D-series

• GPU: NC, ND, NV

• Used for:

  • Enterprise AI

  • Regulated workloads

GCP

• CPU: n2

• GPU: A100, L4

• TPU: v4, v5

• Used for:

  • Large-scale training

  • Research & AI platforms

👉 In interviews:

“We typically separate CPU workloads from GPU workloads using dedicated node pools.”

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4️⃣ 🧠 Connecting This to LLMs & RAG Systems

Typical LLM + RAG Architecture

User Request
   ↓
CPU (FastAPI / Gateway)
   ↓
GPU (Embedding Model)
   ↓
Vector DB (CPU)
   ↓
GPU (LLM Inference)
   ↓
CPU (Post-processing)

Where each processor fits

• CPU

  • API layer

  • Prompt orchestration

  • Retrieval logic

• GPU

  • Embeddings

  • LLM inference

• TPU

  • Model training (if applicable)

• NPU

  • Edge inference (future AI PCs)

👉 Key insight:

CPUs orchestrate the flow, GPUs do the intelligence.

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5️⃣ 🔥 LinkedIn-Friendly Post (Copy–Paste Ready)

🚀 CPU, GPU, TPU, NPU — How Modern AI Platforms Really Work

In real-world AI and MLOps platforms, not all compute is equal.

🧠 CPUs orchestrate pipelines, APIs, and control logic.
⚡ GPUs power model training and high-throughput inference.
🧪 TPUs accelerate large-scale AI training.
📱 NPUs enable low-power, real-time AI on edge devices.

In Kubernetes, GPUs are scheduled explicitly and used only where needed—because idle GPUs are expensive.

In LLM and RAG systems, CPUs handle orchestration and retrieval, while GPUs perform embeddings and inference. Choosing the right compute at each stage is key to performance, cost optimisation, and scalability.

🔑 Takeaway: Successful MLOps isn’t just about models—it’s about matching the right workload to the right compute.

#Kubernetes #MLOps #LLM #RAG #CloudComputing #DevOps #AIInfrastructure