KALKI-1.5

KALKI AI — Deployment, DevOps & Edge Intelligence

1. Kubernetes Production Cluster Architecture

apiVersion: apps/v1
kind: Deployment
metadata:
  name: kalki-backend-api
  namespace: kalki-production
spec:
  replicas: 5
  selector:
    matchLabels:
      app: kalki-backend
  template:
    metadata:
      labels:
        app: kalki-backend
    spec:
      containers:
      - name: api-gateway
        image: kalki/backend:1.5.0
        resources:
          limits:
            cpu: "4"
            memory: "8Gi"
            nvidia.com/gpu: "1"
          requests:
            cpu: "2"
            memory: "4Gi"
        env:
        - name: VECTOR_DB_URL
          value: "http://qdrant.kalki-production:6333"
        ports:
        - containerPort: 8000

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2. Edge SLM Deployment Pipeline

For edge nodes (mobile, smartwatch, IoT):

1. Model Distillation: Distill 70B LLM teacher into 3.8B SLM student (Phi-3 / Llama-3.2).
2. Quantization: INT4 AWQ / GGUF quantization reducing VRAM footprint to <2.2 GB.
3. Execution Runtime: ExecuTorch (Mobile) / ONNX Runtime (Desktop/IoT) / WebGPU (Browser).

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3. Observability & Monitoring Metrics

• Prometheus Collector: Scrapes /metrics for query rates, token latency, and VRAM utilization.
• Grafana Dashboard: Real-time panels for Time-To-First-Token (TTFT), RAG cache hit ratio, and Agent execution success percentage.

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