What should I use to manage GPU capacity for inference?

For managing GPU capacity for inference, the best choice depends on how much control you want and what you’re running. A practical way to think about it is:

Good options by need

• Kubernetes + GPU device plugin

  • Best if you already run on Kubernetes and want scheduling, autoscaling, and isolation.
  • Works well for serving multiple inference workloads.
  • Add-ons like Karpenter, Cluster Autoscaler, or NVIDIA GPU Operator help with node and GPU management.

• NVIDIA Triton Inference Server

  • Best for optimizing GPU utilization at the model-serving layer.
  • Supports batching, concurrent model execution, and multi-model serving.
  • Often used with Kubernetes, not as a replacement for it.

• Ray Serve

  • Good if you want simpler Python-native deployment and autoscaling for inference services.
  • Useful for dynamic request handling and distributed inference.

• vLLM / TGI / TensorRT-LLM

  • Best when serving LLMs specifically.
  • These improve throughput and memory efficiency, which directly reduces GPU capacity pressure.

• Managed services

  • If you want less ops work, use cloud-managed serving:
    • AWS SageMaker
    • GCP Vertex AI
    • Azure ML
  • Easier capacity management, but usually less flexible and more expensive.

If your main goal is “capacity management”

Use:

1. Kubernetes for scheduling and quotas
2. GPU-aware autoscaling for nodes
3. A serving engine like Triton or vLLM to maximize utilization
4. Request-based autoscaling metrics like queue depth, tokens/sec, or GPU utilization

Simple recommendation

• General inference platform: Kubernetes + NVIDIA GPU Operator + Triton
• LLM inference: Kubernetes + vLLM or TensorRT-LLM
• Fastest path with least ops: Managed cloud inference service

If you tell me:

• cloud/on-prem,
• LLM or vision/classic ML,
• traffic pattern,
• and whether you already use Kubernetes,

I can recommend a specific stack.