I'm building on Kubernetes and need help with model serving and GPU scheduling

Absolutely — I can help with both model serving on Kubernetes and GPU scheduling.

To get you to a useful design quickly, here’s the practical breakdown:

1) Model serving on Kubernetes

Common options:

• KServe: great for standardized ML inference on K8s, supports autoscaling, canary, batching, transformers, and multiple runtimes.
• Seldon Core: strong for complex inference graphs, model pipelines, explainers, and custom deployments.
• BentoML: simpler developer experience, often easy to package and deploy.
• Ray Serve: good if you need dynamic Python-based serving, ensembles, or online model composition.
• NVIDIA Triton Inference Server: excellent for high-throughput GPU inference, supports TensorRT, ONNX, PyTorch, TensorFlow, dynamic batching.

If you want:

• simple REST/gRPC serving → BentoML or KServe
• high-performance GPU inference → Triton
• custom pipelines / graphs → Seldon or Ray Serve

2) GPU scheduling on Kubernetes

Typical approaches:

• NVIDIA Kubernetes Device Plugin: required for GPU visibility in pods.
• Node labeling + node affinity: schedule workloads to GPU nodes.
• Taints/tolerations: keep generic workloads off GPU nodes.
• MIG (Multi-Instance GPU): partition supported GPUs into slices for better utilization.
• Time-slicing: share GPUs across workloads when isolation is less strict.
• Priority classes / quotas: control fairness among teams/services.

Basic GPU pod requirements

Your pod typically needs:

• resources.limits.nvidia.com/gpu: 1
• node selector or affinity to GPU nodes
• toleration for GPU node taint if present

Example:

apiVersion: v1
kind: Pod
metadata:
  name: gpu-inference
spec:
  containers:
    - name: app
      image: my-inference-image:latest
      resources:
        limits:
          nvidia.com/gpu: 1
  nodeSelector:
    accelerator: nvidia

3) Recommended production pattern

A common production setup:

• GPU nodes dedicated via taints
• NVIDIA device plugin installed
• Triton or KServe for inference
• Horizontal Pod Autoscaler or KEDA for scaling request-driven services
• Cluster autoscaler to add GPU nodes when demand increases
• MIG if you have A100/H100/L40S class hardware and smaller models

4) Key questions to choose the right setup

If you answer these, I can give you a concrete architecture and YAML:

1. What model framework are you serving? (PyTorch / TensorFlow / ONNX / LLM / custom Python)
2. Do you need GPU inference or just CPU serving?
3. What GPU type do you have? (A10, T4, A100, H100, L4, etc.)
4. Expected traffic: low QPS, steady, or bursty?
5. Do you need autoscaling?
6. Are you deploying one model or many models?
7. Do you want a managed-style API like KServe, or something more custom?

5) If you want, I can help with any of these next:

• a reference Kubernetes deployment for model serving
• GPU node pool + taint/toleration setup
• KServe or Triton deployment YAML
• MIG configuration
• autoscaling strategy
• debugging GPU scheduling issues

If you want, send me:

• your Kubernetes distro (EKS/GKE/AKS/on-prem),
• GPU type,
• serving framework,
• and whether you want KServe or Triton,

and I’ll draft a concrete setup for you.