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Course Outline

Introduction to Scaling Ollama

  • Ollama’s architecture and key scaling considerations.
  • Common bottlenecks encountered in multi-user deployments.
  • Best practices for preparing the infrastructure.

Resource Allocation and GPU Optimization

  • Strategies for efficient CPU/GPU utilization.
  • Considerations for memory and bandwidth.
  • Applying resource constraints at the container level.

Deployment with Containers and Kubernetes

  • Containerizing Ollama using Docker.
  • Running Ollama within Kubernetes clusters.
  • Managing load balancing and service discovery.

Autoscaling and Batching

  • Designing autoscaling policies for Ollama.
  • Utilizing batch inference techniques to optimize throughput.
  • Navigating the trade-offs between latency and throughput.

Latency Optimization

  • Profiling inference performance.
  • Implementing caching strategies and model warm-up.
  • Reducing I/O and communication overhead.

Monitoring and Observability

  • Integrating Prometheus for metrics collection.
  • Constructing dashboards with Grafana.
  • Establishing alerting and incident response for Ollama infrastructure.

Cost Management and Scaling Strategies

  • Implementing cost-aware GPU allocation.
  • Evaluating considerations for cloud versus on-premises deployment.
  • Adopting strategies for sustainable scaling.

Summary and Next Steps

Requirements

  • Experience with Linux system administration.
  • Understanding of containerization and orchestration.
  • Familiarity with machine learning model deployment.

Audience

  • DevOps engineers.
  • ML infrastructure teams.
  • Site reliability engineers.
 21 Hours

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