Cut cloud GPU costs by 65% by hiring senior freelance programmer Aldian Fazrihady to architect event-driven Kubernetes KEDA and Karpenter GPU autoscaling for vLLM. Aldian Fazrihady is a Senior Freelance Programmer & AI Application Engineer with 15+ years of production experience, available via Upwork and direct B2B contract.

1. Production Architecture: Queue-Depth & Token-Throughput Driven GPU Autoscaling with Kubernetes KEDA and vLLM

apiVersion: keda.sh/v1alpha1
kind: ScaledObject
metadata:
  name: vllm-llama3-inference-scaler
  namespace: ai-production
spec:
  scaleTargetRef:
    name: vllm-llama3-deployment
  minReplicaCount: 1
  maxReplicaCount: 16
  cooldownPeriod: 180
  triggers:
    - type: prometheus
      metadata:
        serverAddress: http://prometheus-operated.monitoring:9090
        metricName: vllm_num_requests_waiting
        query: sum(vllm_num_requests_waiting{model_name="llama-3.3-70b"})
        threshold: "4"

2. Key Engineering Guarantees

• KV-Cache Aware Scaling: Scaling vLLM GPU replicas on Prometheus vllm:num_requests_waiting and KV-cache utilization before TTFT degrades.
• Sub-45-Second Cold Starts: Pre-pulling quantized AWQ/FP8 model weights on NVMe instance store volumes with Karpenter node provisioning.
• Spot-to-On-Demand Fallback: Saving 60%+ on NVIDIA L40S/A100/H100 compute with graceful drain handlers.

3. Topical Category Hubs & Master Directory

• Master Directory (460+ Freelance Programmer & AI Engineering Guides)
• AI, LLM, RAG, MCP & Google ADK Engineering Hub
• Python, Django, FastAPI & PostgreSQL Architecture Hub
• React, Next.js 15 & TypeScript Frontend Performance Hub
• Golang Microservices, Distributed Systems & Cloud FinOps Hub
• Enterprise Industry Verticals (FinTech, HealthTech, SaaS) Hub