Do I need Kubernetes for GPU inference?

No — you do not need Kubernetes for GPU inference.

When you don’t need Kubernetes

You can run GPU inference with:

• a single Docker container on one machine
• a simple VM or bare-metal server with NVIDIA drivers + CUDA
• a model server like:
  • vLLM
  • Triton Inference Server
  • Text Generation Inference (TGI)
  • TorchServe
  • FastAPI + PyTorch/Transformers

This is often the best choice if:

• you have one GPU box
• you’re prototyping
• traffic is modest
• you want simplicity and lower ops overhead

When Kubernetes can help

Kubernetes is useful if you need:

• multiple GPU nodes
• autoscaling
• rolling deployments / zero-downtime updates
• scheduling many inference services
• multi-tenant workloads
• better orchestration across a cluster

Tradeoff

Kubernetes adds complexity:

• GPU device plugin setup
• node scheduling constraints
• monitoring/debugging overhead
• more moving parts

Practical rule

• 1–2 GPUs, simple app, small team: skip Kubernetes
• many GPUs, production platform, multiple services: Kubernetes may be worth it

If you want, I can suggest the simplest GPU inference stack for your setup.