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Duration 21 hours
Course Outline
Introduction to Scaling Ollama
- Ollama’s architecture and key scaling factors
- Identifying common bottlenecks in multi-user setups
- Best practices for preparing infrastructure
Resource Allocation and GPU Optimization
- Strategies for efficient CPU/GPU utilization
- Considerations for memory and bandwidth
- Managing container-level resource limits
Deployment with Containers and Kubernetes
- Containerizing Ollama using Docker
- Operating Ollama within Kubernetes clusters
- Implementing load balancing and service discovery
Autoscaling and Batching
- Developing autoscaling policies for Ollama
- Batch inference techniques to boost throughput
- Managing the balance between latency and throughput
Latency Optimization
- Profiling inference performance
- Employing caching strategies and model warm-up
- Minimizing I/O and communication overhead
Monitoring and Observability
- Integrating Prometheus for metric collection
- Creating dashboards with Grafana
- Setting up alerting and incident response for Ollama infrastructure
Cost Management and Scaling Strategies
- Cost-effective GPU allocation methods
- Evaluating cloud versus on-premises deployment options
- Approaches for sustainable scaling
Summary and Next Steps
Requirements
- Proficiency in Linux system administration
- Knowledge of containerization and orchestration concepts
- Understanding of machine learning model deployment
Target Audience
- DevOps engineers
- ML infrastructure teams
- Site reliability engineers
