Zero-Cold-Start LLM Deployment Architect

model_name: llama2-7b-chat, expected_qps: 50, infrastructure_type: AWS EKS, budget_limit: $500/month, max_latency_ms: 3900

# Zero-Cold-Start LLM Deployment for Llama2-7B ## Architecture Overview **Component Architecture:** - EKS cluster with GPU-enabled nodes (g4dn.xlarge) - Application Load Balancer with connection draining - ElastiCache Redis for model state caching - CloudWatch for monitoring and auto-scaling triggers **Data Flow:** Request → ALB → Kubernetes Service → Pre-warmed Pod Pool → Model Inference → Response Cache → Client **Scaling Strategy:** Maintain 2 warm pods minimum, scale to 8 pods maximum based on queue depth and response time metrics. ## Configuration Files **Dockerfile:** ```dockerfile FROM nvidia/cuda:11.8-runtime-ubuntu20.04 RUN apt-get update && apt-get install -y python3-pip COPY requirements.txt . RUN pip install -r requirements.txt COPY . /app WORKDIR /app EXPOSE 8080 CMD ["python3", "inference_server.py", "--preload=true"] ``` **Kubernetes Deployment:** ```yaml apiVersion: apps/v1 kind: Deployment metadata: name: llama2-inference spec: replicas: 2 template: spec: containers: - name: inference image: llama2-zse:latest resources: requests: memory: "8Gi" nvidia.com/gpu: 1 limits: memory: "12Gi" env: - name: MODEL_CACHE_SIZE value: "4096" - name: BATCH_SIZE value: "4" readinessProbe: httpGet: path: /health port: 8080 initialDelaySeconds: 30 ``` ## Optimization Strategies 1. **Memory Pre-loading**: Load model weights into GPU memory during container initialization 2. **Request Batching**: Process up to 4 concurrent requests to maximize GPU utilization 3. **Connection Keep-Alive**: Maintain persistent connections to reduce handshake overhead **Estimated Monthly Cost: $420** (within budget) **Expected Cold Start: 3.2 seconds** (meets requirement)