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Large Language Model (LLM) Deployment Guide

Overview​

This guide provides best practices for deploying Large Language Models (LLMs) on TKE, covering model serving, resource optimization, and scaling strategies.

Model Serving Patterns​

1. Online Serving​

  • Real-time inference: Low-latency model serving
  • API endpoints: RESTful API interfaces
  • Load balancing: Horizontal scaling for high concurrency

2. Batch Inference​

  • Offline processing: High-throughput batch inference
  • Data pipeline integration: Integration with data processing workflows

Resource Configuration​

GPU Resources​

resources:
  limits:
    nvidia.com/gpu: 4
    memory: 64Gi
    cpu: 16
  requests:
    nvidia.com/gpu: 4
    memory: 64Gi
    cpu: 16

Memory Optimization​

  • Model quantization techniques
  • Memory-efficient attention mechanisms
  • Gradient checkpointing

Scaling Strategies​

Horizontal Scaling​

  • Deploy multiple model replicas
  • Use Kubernetes HPA for automatic scaling
  • Implement request routing and load balancing

Vertical Scaling​

  • Optimize single-instance performance
  • Use larger GPU instances for complex models
  • Memory optimization techniques

Monitoring and Observability​

Key Metrics​

  • Inference latency (P50, P95, P99)
  • Throughput (requests per second)
  • GPU utilization and memory usage
  • Error rates and success rates

Monitoring Tools​

  • Prometheus for metrics collection
  • Grafana for visualization
  • Custom dashboards for LLM-specific metrics

Security Considerations​

Model Security​

  • Model encryption and access control
  • API authentication and authorization
  • Input validation and sanitization

Data Privacy​

  • Data encryption in transit and at rest
  • Compliance with data protection regulations
  • Secure model deployment practices

Best Practices​

Performance Optimization​

  • Use model compilation and optimization tools
  • Implement caching mechanisms
  • Optimize network communication

Cost Optimization​

  • Use spot instances for non-critical workloads
  • Implement auto-scaling based on demand
  • Monitor and optimize resource utilization